Personalised Cancer Diagnosis
Given past patient diagnosis records and virus mutation wikipedia. We have to find out which virus new patient carries now from category of 10 virus mutations?
- 1. Business Problem
- 2. Machine Learning Problem Formulation
- Importing the required libraries
- 3. Exploratory Data Analysis
- 3.1 Univariate Analysis</p> </div> </div> </div> 3.1.2 Analysing Gene Feature </p> </div> </div> </div> Q1. what type of feature is gene ? </div> </div> </div> It is a Categorical Feature Q2.How many Categories are there? </div> </div> </div> df.groupby("Gene")["ID"].count() Gene ABL1 26 ACVR1 3 AGO2 5 AKT1 28 AKT2 11 .. WHSC1 1 WHSC1L1 1 XPO1 2 XRCC2 2 YAP1 4 Name: ID, Length: 264, dtype: int64 we can see that there are 263 unique Gene categories are present Q3. Distribution of Gene? </div> </div> </div> unique_genes = df['Gene'].value_counts() # the top 10 genes that occured most print(unique_genes.head(10)) BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 KIT 99 BRAF 93 ALK 69 ERBB2 69 PDGFRA 60 Name: Gene, dtype: int64 sums=sum(unique_genes.values) result=unique_genes.values/sums plt.plot(result,label="Histrogram of Genes") plt.xlabel('Index of a Gene') plt.ylabel('Number of Occurances') plt.legend() plt.grid() plt.show() There are some genes which occur very less , some genes which occur more plt.plot(np.cumsum(result),label='Cumulative distribution of Genes') plt.grid() plt.legend() plt.show() 80 Percent of the genes have index in range 50-75 Q4 How to featurize this Feature? Gene </div> </div> </div> 1.One-Hot Encoding 2.Mean-ResponseCoding 3.FeatureHashing source:-https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher </div> </div> </div> Lets Create data frames to work with this df_one_hot_encoding=df df_mean_response_coding=df df_featureHashing=df OneHotEncoding df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text']) df_one_hot_encoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene 0 FAM58A 1 CBL 2 CBL 3 CBL 4 CBL labelencode=LabelEncoder() df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene']) df_one_hot_encoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene Generated_lables 0 FAM58A 85 1 CBL 39 2 CBL 39 3 CBL 39 4 CBL 39 ... ... ... 3316 RUNX1 221 3317 RUNX1 221 3318 RUNX1 221 3319 RUNX1 221 3320 RUNX1 221 3321 rows × 2 columns list_feature_labels=list(labelencode.classes_) df_one_hot_encoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene Generated_lables 0 FAM58A 85 1 CBL 39 2 CBL 39 3 CBL 39 4 CBL 39 onehotencoder=OneHotEncoder() array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray() data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels) data_gen.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... SDHB SDHC SETD2 SF3B1 SHOC2 SHQ1 SMAD2 SMAD3 SMAD4 SMARCA4 SMARCB1 SMO SOS1 SOX9 SPOP SRC SRSF2 STAG2 STAT3 STK11 TCF3 TCF7L2 TERT TET1 TET2 TGFBR1 TGFBR2 TMPRSS2 TP53 TP53BP1 TSC1 TSC2 U2AF1 VEGFA VHL WHSC1 WHSC1L1 XPO1 XRCC2 YAP1 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 264 columns onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 ) onehotencoded_features_gene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables']) finalOneHotEncodedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene). finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv") Feature Hashing for GeneFeature We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes. df_featureHashing.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text']) df_featureHashing.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene 0 FAM58A 1 CBL 2 CBL 3 CBL 4 CBL hasher=FeatureHasher(n_features=9,input_type='string') hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray() dataframe_hashed_features=pd.DataFrame(hased_features) dataframe_hashed_features.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1) finalFeatureHashedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv") Now Lets go for meanResponseCoding df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... df_mean_response_coding.Gene.value_counts() BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 ... DUSP4 1 TCF7L2 1 VEGFA 1 ASXL1 1 ARID1A 1 Name: Gene, Length: 264, dtype: int64 df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text']) df_mean_response_coding.Gene.value_counts() BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 ... IKZF1 1 GNA11 1 INPP4B 1 FGF3 1 ARID1A 1 Name: Gene, Length: 264, dtype: int64 df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... len(df) 3321 dummy=df_mean_response_coding[0:3321] objects=dummy.Gene.value_counts() def myfunction_one(data): dummy1=data my_dicti={} for i,denominator in objects.items(): vector=[] for k in range (1,10): count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)]) vector.append( (count + (1*10)) / (denominator + (1*90)) ) my_dicti[i] = vector return my_dicti my_diciti=myfunction_one(dummy) def vectoresCreation(data): dummy1=data eachrow_vector_data=[ ] for index,row in dummy1.iterrows(): if row["Gene"] in dict(dummy1.Gene.value_counts()).keys(): eachrow_vector_data.append(my_diciti[row["Gene"]]) else : eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9]) return(eachrow_vector_data) eachRow_Vector=vectoresCreation(dummy) df_meanresponse_vectors=pd.DataFrame(eachRow_Vector) df_meanresponse_vectors.shape (3321, 9) df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1) df_meanresponse_vectors.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv") Q5.How good is this gene feature in predicting y_i? We can do this in many ways one is by simple plotting and other by simple models. Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well. finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0') finalFeatureHashedFeaturesOfGene.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 4 finalFeatureHashedFeaturesOfGene.shape (3321, 10) finalFeatureHashedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]] model to test def SVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) SVM(X_Hashed,y_true_Hashed) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) by seeing the above plot we take alpha as 0.001 def SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test)) SVMModel(X_Hashed,y_true_Hashed) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logg loss for training data with best aplha 0.001 is [1.7364535644919004] The Logg loss for cv data with best aplha 0.001 is [1.7737982623403665] The Logg loss for test data with best aplha 0.001 is [1.7470137046208585] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well lets go to meanresponse coding features and apply SVM finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0') finalMeanResponseVectorsOfGene.columns Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']] y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values def SVM_meanResponseCoding(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True) #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) We ca see that i can take c = 1 from above graph Lets go for testing on test data def SVM_meanResponseCoding_test(var1,var2): """ This function is use to build n test SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True) #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) predict_y3 =calibrated.predict_proba(x_test) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train)) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logg loss for training data with best aplha 1 is [1.1284981651581154] The Logg loss for cv data with best aplha 1 is [1.1436551747978385] The Logg loss for test data with best aplha 1 is [1.1082205650636348] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well. comming to Onehotencodded Features we can use logistic regression due to high dimentions finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0") finalOneHotFeatures.columns Index(['ABL1', 'ACVR1', 'AGO2', 'AKT1', 'AKT2', 'AKT3', 'ALK', 'APC', 'AR', 'ARAF', ... 'TSC2', 'U2AF1', 'VEGFA', 'VHL', 'WHSC1', 'WHSC1L1', 'XPO1', 'XRCC2', 'YAP1', 'Class'], dtype='object', length=265) x_oneHot=finalOneHotFeatures.iloc[:,0:264 ] y_true_oneHot=finalOneHotFeatures.Class.values Model def LogisticReg_tune(var1,var2): """ This Function is used to tune the logistic regression """ X=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train = [] logLoss_cv = [] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ] for i in c: clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) clf.fit(x_train , y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) LogisticReg_tune(x_oneHot,y_true_oneHot) (2124, 264) (2124,) (532, 264) (532,) (665, 264) (665,) by seeing the above graph i can take log loss as 0.1 . def LogisticReg_test(var1,var2): """ This Function is used to tune the logistic regression """ X=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train = [] logLoss_cv = [ ] logLoss_test=[ ] clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) clf.fit(x_train , y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) predict_y3 = calibrated.predict_proba(x_test) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train)) print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv )) print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test)) LogisticReg_test(x_oneHot,y_true_oneHot) (2124, 264) (2124,) (532, 264) (532,) (665, 264) (665,) The Logg loss for training data with best aplha 0.1 is [1.2638620900916107] The Logg loss for cv data with best aplha 0.1 is [1.3524898055298527] The Logg loss for test data with best aplha 0.1 is [1.3393455550993199] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well. Observations on Gene Featurisations table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470]) table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082]) table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Gene | FeatureHaser | SVM-kernel-RBF | 0.001 | 1.7364 | 1.7737 | 1.747 | | Gene | MeanResponseCoding | SVM-kernel-RBF | 1 | 1.1284 | 1.1436 | 1.1082 | | Gene | OneHotEncoding | LogisticRegression | 0.1 | 1.2638 | 1.3524 | 1.3393 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ SVM with less dimensions with meanResponseCoding worked well because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel. Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model. Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.3 Analysing of Variation Feature</p> </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... Q1. what is the Variation Feature Type? </div> </div> </div> It is Categorical </div> </div> </div> Q2.How many categories are present? </div> </div> </div> len(df.Variation.value_counts()) 2996 There are 2996 categories </div> </div> </div> Q3.What is the distribution of categories? </div> </div> </div> unique_variations=df.Variation.value_counts() sum0f_unique=sum(unique_variations) histoGram_variation=unique_variations.values / sum0f_unique plt.plot(histoGram_variation ,label = "Histogram of Variation") plt.xlabel(" index of Variation") plt.ylabel(" count") plt.legend() plt.grid() plt.show() by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0. cumsum_histogram=np.cumsum(histoGram_variation) plt.plot(cumsum_histogram,label='Cumulative distribution of variations') plt.grid() plt.legend() plt.show() Q4.How to Featurise this variation Feature? </div> </div> </div> There are few ways:- 1.OneHotEncoding 2.FeatureHasher 3.MeanResponseCoding </div> </div> </div> Lets Start with OneHotEncoding of VariationFeature ds_oneHotencoding=df ds_meanResponse_coding=df ds_featureHasher=df ds_oneHotencoding.columns Index(['ID', 'Gene', 'Variation', 'Class', 'Cleaned_text'], dtype='object') ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text']) ds_oneHotencoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation 0 Truncating Mutations 1 W802* 2 Q249E 3 N454D 4 L399V label_encoder=LabelEncoder() ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"]) ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns featurenames=label_encoder.classes_ ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns onehotencoder_Variation=OneHotEncoder() encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray() encoded_array array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]) ds_dash=pd.DataFrame(encoded_array , columns=featurenames) ds_dash.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insGLYVDFREYEY Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 2996 columns OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1) OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues']) OneHotEncoded_ds.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv") OneHotEncoded_ds .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3321 rows × 2997 columns Lets create feature hasher for variation features ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"]) ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Class 0 Truncating Mutations 1 1 W802* 2 2 Q249E 2 3 N454D 3 4 L399V 4 hasher= FeatureHasher(n_features=9,input_type='string') array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray() ds_dash_hashed=pd.DataFrame(array_hashed) hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1) hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv") Lets go for mean responsecoding of variation feature df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... objects_ThisFeature=df_mean_response_coding.Variation.value_counts() objects_ThisFeature Truncating Mutations 93 Deletion 74 Amplification 71 Fusions 34 Overexpression 6 .. K78A 1 A1789S 1 T401I 1 K550_V555delinsI 1 R420H 1 Name: Variation, Length: 2996, dtype: int64 my_dictionary_varFeature={ } for feature_name , feature_total_count in objects_ThisFeature.items(): vector_array_features=[ ] for index in range(1,10): count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)]) vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90))) my_dictionary_varFeature[feature_name] = vector_array_features my_dictionary_varFeature {'Truncating Mutations': [0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], 'Deletion': [0.40853658536585363, 0.06097560975609756, 0.06097560975609756, 0.15853658536585366, 0.06097560975609756, 0.06707317073170732, 0.06097560975609756, 0.06097560975609756, 0.06097560975609756], 'Amplification': [0.062111801242236024, 0.19254658385093168, 0.062111801242236024, 0.062111801242236024, 0.062111801242236024, 0.12422360248447205, 0.3105590062111801, 0.062111801242236024, 0.062111801242236024], 'Fusions': [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], 'Overexpression': [0.10416666666666667, 0.125, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.14583333333333334, 0.10416666666666667, 0.10416666666666667], 'G12V': [0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.14893617021276595, 0.10638297872340426, 0.10638297872340426], 'Q61H': [0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011], 'Q61L': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'E17K': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'T58I': [0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011], 'Q61R': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'P34R': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Q22K': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'S308A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Promoter Hypermethylation': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'F384L': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'E330K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G12C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Y42C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G67R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G13V': [0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'P130S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Q209L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'T286A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G12A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'I31M': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'A146V': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'E542K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'TMPRSS2-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'M1R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G35R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304], 'Y64A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R173C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'ETV6-NTRK3 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G13C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R170W': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G13D': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'K117N': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'C618R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'F28L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'Q61K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S222D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T73I': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R841K': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'V321M': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T167A': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'A146T': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'EWSR1-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S501_A502dup': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R287A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A57V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G309A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'W131G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V555_V559del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R683K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A18D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E239A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E439del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R776C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S241L': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'FGFR1-TACC1 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G42R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T160I': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'ROS1-CD74 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R373H': [0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N546K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R465H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V1676D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1686Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I122S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y599_D600insSTDNEYFYVDFREYEY': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1172L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R487W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L838P': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E127G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'L117P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T674I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P278A': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W24C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'X963_splice': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R2336H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A2770T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P81T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S464L': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D331G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A750_E758del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C482R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S36Y': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V465M': 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0.10989010989010989], 'L424V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V774A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H1094R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], '2010_2471trunc': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D816E': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'P551_V555del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T315I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K292T': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G1128S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T123A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], 'I744_K745delinsKIPVAI': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G81D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1841N': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D839G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1094Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 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0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C91S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V242F': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y553N': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D289_D292del': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K45T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M774_A775insAYVM': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'DNMT3B7': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989], 'S33Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G67S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G596C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H36P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'Y652H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'I1018W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V769_D770insGVV': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R282W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V11A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R420Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E82V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P186S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V155F': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G380R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C135R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S35Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T205A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746_A750del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D83V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K181M': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088], 'GIT2-PDGFRB Fusion': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T417I': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R80P': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R1097H': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I1170S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R283Q': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E1071W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P114L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F74S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R905Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W383R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R905W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N372H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'DNA binding domain deletions': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D473G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K550_W557del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T529N': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H61D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M1255I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N116H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L747_P753del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P480L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T1691K': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L1947R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E172K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V344A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L248V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G129E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D408E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F119S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H597Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'EGFRvIII': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E40K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A1752V': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G248V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989]] meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss) meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1) meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv") As we have all the vector forms of the featurisations lets proceed with further analysis Q5.How good is this Variation feature in predicting y_i? Lets Build simple models to check our selves As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library. 1.meanResponse coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> SVM Model meanResponseCoding_variationFeature.columns Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 'Class'], dtype='object') x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values x_true_meanresponse array([[0.54098361, 0.06010929, 0.05464481, ..., 0.05464481, 0.05464481, 0.05464481], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], ..., [0.09677419, 0.33064516, 0.08064516, ..., 0.08064516, 0.08870968, 0.08064516], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011]]) def svm_model_tuning(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) svm_model_tuning(x_true_meanresponse , y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) From above graph i select my best C as 1 Testing of the model def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) svm_model_testing(x_true_meanresponse,y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 1 the coresponding train loss is [0.13402600125222605] The Logloss for 1 the coresponding cv loss is [0.1479095316786708] The Logloss for 1 the coresponding test loss is [0.12267536305037746] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 2.Hashed coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0') hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values y_hashed_true=hashedEncodedFeatureof_variation['Class'].values We will reuse the same svm functions we defiend earilier svm_model_tuning(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) from the above graph i can choose my C as 0.0001 def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test )) svm_model_testing(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 0.0001 the coresponding train loss is [1.7335822874941234] The Logloss for 0.0001 the coresponding cv loss is [1.753463388538884] The Logloss for 0.0001 the coresponding test loss is [1.7599055604165221] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 3.OneHotencoding of variation feature As this has high dimention lets use logistic regression </div> </div> </div> oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0') oneHotEncodedfeaturesof_Variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns oneHotEncodedfeaturesof_Variation.columns Index(['1_2009trunc', '2010_2471trunc', '256_286trunc', '3' Deletion', '385_418del', '422_605trunc', '533_534del', '534_536del', '550_592del', '560_561insER', ... 'Y901C', 'Y931C', 'Y98H', 'Y98N', 'YAP1-FAM118B Fusion', 'YAP1-MAMLD1 Fusion', 'ZC3H7B-BCOR Fusion', 'ZNF198-FGFR1 Fusion', 'p61BRAF', 'Class'], dtype='object', length=2997) x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values LogisticModel def logistic_tune(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) logistic_tune(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) From the above graph i can choos aplha as 0.01 Testing model def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test )) logistic_test(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) The Logloss for 0.01 the coresponding train loss is [1.4238762684713966] The Logloss for 0.01 the coresponding cv loss is [1.744474382727282] The Logloss for 0.01 the coresponding test loss is [1.7380242209450858] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets compare all of them Lets compare all observations on Variation Feature table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599]) table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479]) table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Variation | FeatureHaser | SVM-kernel-RBF | 0.0001 | 1.7335 | 1.7534 | 1.7599 | | Variation | MeanResponseCoding | SVM-kernel-RBF | 1 | 0.134 | 0.1479 | 0.1479 | | Variation | OneHotEncoding | LogisticRegression | 0.01 | 1.4238 | 1.7444 | 1.738 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well. Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.4 Analysing of Text Feature</p> </div> </div> </div> Q1.What kind of feature is Text </div> </div> </div> It is sentance corpus consisting of many sentances. Q2.how can we featurise it There are many ways we will work with these two 1.BOW 2.TFIDF </div> </div> </div> 1.Featurising with bow df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... bow_df=df bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation']) array_input=bow_df.Cleaned_text.values bow_ds=df vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000) tf=vectoriser.fit(array_input) vocab_countVectoriser=(tf.vocabulary_) vocab_countVectoriser.items() dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 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'concept', 'conclude', 'concluded', 'conclusion', 'conclusions', 'concomitant', 'concordance', 'concurrent', 'condition', 'conditional', 'conditions', 'conducted', 'confer', 'confer resistance', 'conferred', 'conferring', 'confers', 'confidence', 'confirm', 'confirmation', 'confirmed', 'confirming', 'confluence', 'conformation', 'conformational', 'conformational change', 'conformational changes', 'conformations', 'congenital', 'conjugated', 'consecutive', 'consensus', 'consent', 'consequence', 'consequences', 'consequently', 'conservation', 'conservative', 'conserved', 'consider', 'considerable', 'considered', 'considering', 'consisted', 'consistent', 'consistent previous', 'consistently', 'consisting', 'consists', 'consortium', 'constant', 'constitute', 'constitutional', 'constitutive', 'constitutive activation', 'constitutively', 'constitutively activated', 'constitutively active', 'construct', 'constructed', 'construction', 'constructs', 'contact', 'contact help', 'contacts', 'contain', 'contained', 'containing', 'containing mm', 'contains', 'content', 'context', 'contexts', 'continued', 'continuous', 'contrast', 'contribute', 'contributed', 'contributes', 'contributing', 'contribution', 'control', 'control cells', 'controlled', 'controlling', 'controls', 'conventional', 'conversely', 'conversion', 'cooh', 'cooh terminal', 'cooperate', 'cooperative', 'copies', 'copy', 'copy number', 'core', 'core domain', 'core enzyme', 'correct', ...] array_transformed=vectoriser.transform(array_input) array_transformedone=array_transformed.toarray() dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow ) dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1) dataframofBowForText.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aa aacrjournals aacrjournals org ab abbreviations abc abd aberrant aberrant splicing aberrations abi ability ability bind ability induce abl able abnormal abnormalities abolish abolished abrogate abrogated absence absent absolute abstract abundance abundant ac acc accelerated accepted acceptor access access image accessible accessible alternative accession accompanied accordance ... withdrawal within without wm wm cells wnt women work worldwide worse wpd written wt wt mutant wt wt wtb wtb raf wu xenograft xenografts xenopus xl xp xrcc xu yap year years yeast yeast cells yellow yes yet yield yielded young zhang zinc zn Class 0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 ... 0 3 3 0 0 0 0 2 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 3 0 0 0 0 1 0 0 2 0 0 1 1 0 0 3 2 0 8 0 1 0 1 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ... 2 2 5 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 4 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 ... 0 4 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 10 4 5 rows × 5001 columns dataframofBowForText.to_csv("datafraneofBowTextFeature.csv") 2.TFIDF featurisation of text data </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... tfidf_df=df tfidf_ds=df tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class']) tfidf_df .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Cleaned_text 0 cyclin dependent kinases cdks regulate variety... 1 abstract background non small cell lung cancer... 2 abstract background non small cell lung cancer... 3 recent evidence demonstrated acquired uniparen... 4 oncogenic mutations monomeric casitas b lineag... ... ... 3316 introduction myelodysplastic syndromes mds het... 3317 introduction myelodysplastic syndromes mds het... 3318 runt related transcription factor gene runx al... 3319 runx gene frequent target chromosomal transloc... 3320 frequent mutations associated leukemia recurre... 3321 rows × 1 columns array_input_tfidf=tfidf_df['Cleaned_text'].values vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000) vectorizer.fit(array_input_tfidf) TfidfVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.float64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=1000, min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=True, stop_words=None, strip_accents=None, sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True, vocabulary=None) len(vectorizer.vocabulary_) 1000 featurenames_tfidf=vectorizer.get_feature_names() tfidf_vocab=vectorizer.vocabulary_ transformed_tfidf_csr=vectorizer.transform(array_input_tfidf) transformed_tfidf_csr=transformed_tfidf_csr.toarray() arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf) tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1) tfidf_textfeature.to_csv("tfidf_textfeatures.csv") tfidf_textfeature .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional adenocarcinoma advanced affect affected affecting affinity age akt al ala alk allele alleles alone alterations altered alternative although ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 0.000000 0.0 0.000000 0.007996 0.003394 0.003430 0.000000 0.000000 0.000000 0.0 0.0 0.005836 0.000000 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0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 0.000000 0.0 0.000000 0.026962 0.007630 0.000000 0.003861 0.000000 0.000000 0.0 0.0 0.009838 0.018076 0.003339 0.000000 0.000000 0.000000 0.017664 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 0.000000 0.0 0.010418 0.009430 0.007205 0.031548 0.000000 0.000000 0.000000 0.0 0.0 0.008259 0.000000 0.000000 0.000000 0.000000 0.004647 0.006950 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 0.000000 0.0 0.001547 0.000000 0.000000 0.000000 0.004330 0.000000 0.006224 0.0 0.0 0.000000 0.000000 0.013106 0.003385 0.000000 0.000000 0.004953 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 0.000000 0.0 0.001989 0.000000 0.000000 0.000000 0.005570 0.000000 0.008005 0.0 0.0 0.000000 0.000000 0.016859 0.002177 0.000000 0.000000 0.006371 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.003138 0.000000 0.000000 0.005647 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.001402 0.001502 0.002697 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 0.000000 0.0 0.000000 0.018975 0.000000 0.002713 0.005434 0.000000 0.093724 0.0 0.0 0.013848 0.005089 0.011748 0.025491 0.000000 0.023376 0.004662 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 0.000000 0.0 0.003222 0.025663 0.000000 0.009006 0.003006 0.000000 0.055096 0.0 0.0 0.010216 0.004223 0.010400 0.015279 0.000000 0.014371 0.005158 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1001 columns Q3. Is this Bow,tfidf for text feature Useful in our classification? </div> </div> </div> Lets find out by tsne and one simple model. TSNE for BOW TEXT Feature x_std=dataframofBowForText.iloc[:, 0:5000].values y_true_std=dataframofBowForText.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_std) intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df10,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Tsne for TFIDF data x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values y_tfidf_true=tfidf_textfeature.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_tfidf_true) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df12,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Lets do analisys based on models for the two featurisations and compare the results Lets go with simple model like logistic regression for high dimension data 1.BOW Feature for text def oneHot_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) oneHot_Logsitic( data,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data oneHot_Logsitic( x_std,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one is without standardised data from the graphs i can take my alpha as 0.001 Testing on test datasets def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test )) logistic_test(x_std,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0724656346673704] The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368] The Logloss for 0.001 the coresponding test loss is [1.3034431218014317] Above is result on normal data logistic_test(data,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9441582057917238] The Logloss for 0.001 the coresponding cv loss is [1.210222073584811] The Logloss for 0.001 the coresponding test loss is [1.1539667998430334] Above is Result on standardised data we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well. 2.TFIDF feauture for text Using Logisticmodels we bult for previous cases Tuning oneHot_Logsitic(x_tfidf_true,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was without standardised data oneHot_Logsitic(data,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data From the graphs i can select my alpha as 0.001 Lets Test the model without standardised data logistic_test(x_tfidf_true,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0655639055266641] The Logloss for 0.001 the coresponding cv loss is [1.287554414069922] The Logloss for 0.001 the coresponding test loss is [1.2673057224719144] with standardised data logistic_test(data,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9062325983187661] The Logloss for 0.001 the coresponding cv loss is [1.154476481391309] The Logloss for 0.001 the coresponding test loss is [1.2146295572433117] Observations of Text featureised models with Standardised data only</p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 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-0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p>
</div>
</div>
</div>
3.1.2 Analysing Gene Feature </p>
</div>
</div>
</div>
Q1. what type of feature is gene ?
</div>
</div>
</div>
It is a Categorical Feature
Q2.How many Categories are there?
</div>
</div>
</div>
df.groupby("Gene")["ID"].count()
Gene
ABL1 26
ACVR1 3
AGO2 5
AKT1 28
AKT2 11
..
WHSC1 1
WHSC1L1 1
XPO1 2
XRCC2 2
YAP1 4
Name: ID, Length: 264, dtype: int64
we can see that there are 263 unique Gene categories are present
Q3. Distribution of Gene?
</div>
</div>
</div>
unique_genes = df['Gene'].value_counts()
# the top 10 genes that occured most
print(unique_genes.head(10))
BRCA1 264
TP53 163
EGFR 141
PTEN 126
BRCA2 125
KIT 99
BRAF 93
ALK 69
ERBB2 69
PDGFRA 60
Name: Gene, dtype: int64
sums=sum(unique_genes.values)
result=unique_genes.values/sums
plt.plot(result,label="Histrogram of Genes")
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.grid()
plt.show()
There are some genes which occur very less , some genes which occur more
plt.plot(np.cumsum(result),label='Cumulative distribution of Genes')
plt.grid()
plt.legend()
plt.show()
80 Percent of the genes have index in range 50-75
Q4 How to featurize this Feature? Gene
</div>
</div>
</div>
1.One-Hot Encoding
2.Mean-ResponseCoding
3.FeatureHashing
source:-https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher
</div>
</div>
</div>
Lets Create data frames to work with this
df_one_hot_encoding=df
df_mean_response_coding=df
df_featureHashing=df
OneHotEncoding
df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text'])
df_one_hot_encoding.head()
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Gene
0
FAM58A
1
CBL
2
CBL
3
CBL
4
CBL
labelencode=LabelEncoder()
df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene'])
df_one_hot_encoding
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Gene
Generated_lables
0
FAM58A
85
1
CBL
39
2
CBL
39
3
CBL
39
4
CBL
39
...
...
...
3316
RUNX1
221
3317
RUNX1
221
3318
RUNX1
221
3319
RUNX1
221
3320
RUNX1
221
3321 rows × 2 columns
list_feature_labels=list(labelencode.classes_)
df_one_hot_encoding.head()
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Gene
Generated_lables
0
FAM58A
85
1
CBL
39
2
CBL
39
3
CBL
39
4
CBL
39
onehotencoder=OneHotEncoder()
array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray()
data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels)
data_gen.head()
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ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
SDHB
SDHC
SETD2
SF3B1
SHOC2
SHQ1
SMAD2
SMAD3
SMAD4
SMARCA4
SMARCB1
SMO
SOS1
SOX9
SPOP
SRC
SRSF2
STAG2
STAT3
STK11
TCF3
TCF7L2
TERT
TET1
TET2
TGFBR1
TGFBR2
TMPRSS2
TP53
TP53BP1
TSC1
TSC2
U2AF1
VEGFA
VHL
WHSC1
WHSC1L1
XPO1
XRCC2
YAP1
0
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5 rows × 264 columns
onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 )
onehotencoded_features_gene.Class.value_counts()
7 953
4 686
1 568
2 452
6 275
5 242
3 89
9 37
8 19
Name: Class, dtype: int64
finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables'])
finalOneHotEncodedFeaturesOfGene.Class.value_counts()
7 953
4 686
1 568
2 452
6 275
5 242
3 89
9 37
8 19
Name: Class, dtype: int64
As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene).
finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv")
Feature Hashing for GeneFeature
We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes.
df_featureHashing.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text'])
df_featureHashing.head()
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Gene
0
FAM58A
1
CBL
2
CBL
3
CBL
4
CBL
hasher=FeatureHasher(n_features=9,input_type='string')
hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray()
dataframe_hashed_features=pd.DataFrame(hased_features)
dataframe_hashed_features.head()
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0
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8
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finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1)
finalFeatureHashedFeaturesOfGene.Class.value_counts()
7 953
4 686
1 568
2 452
6 275
5 242
3 89
9 37
8 19
Name: Class, dtype: int64
finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv")
Now Lets go for meanResponseCoding
df_mean_response_coding.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
df_mean_response_coding.Gene.value_counts()
BRCA1 264
TP53 163
EGFR 141
PTEN 126
BRCA2 125
...
DUSP4 1
TCF7L2 1
VEGFA 1
ASXL1 1
ARID1A 1
Name: Gene, Length: 264, dtype: int64
df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text'])
df_mean_response_coding.Gene.value_counts()
BRCA1 264
TP53 163
EGFR 141
PTEN 126
BRCA2 125
...
IKZF1 1
GNA11 1
INPP4B 1
FGF3 1
ARID1A 1
Name: Gene, Length: 264, dtype: int64
df.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
len(df)
3321
dummy=df_mean_response_coding[0:3321]
objects=dummy.Gene.value_counts()
def myfunction_one(data):
dummy1=data
my_dicti={}
for i,denominator in objects.items():
vector=[]
for k in range (1,10):
count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)])
vector.append( (count + (1*10)) / (denominator + (1*90)) )
my_dicti[i] = vector
return my_dicti
my_diciti=myfunction_one(dummy)
def vectoresCreation(data):
dummy1=data
eachrow_vector_data=[ ]
for index,row in dummy1.iterrows():
if row["Gene"] in dict(dummy1.Gene.value_counts()).keys():
eachrow_vector_data.append(my_diciti[row["Gene"]])
else :
eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
return(eachrow_vector_data)
eachRow_Vector=vectoresCreation(dummy)
df_meanresponse_vectors=pd.DataFrame(eachRow_Vector)
df_meanresponse_vectors.shape
(3321, 9)
df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1)
df_meanresponse_vectors.Class.value_counts()
7 953
4 686
1 568
2 452
6 275
5 242
3 89
9 37
8 19
Name: Class, dtype: int64
df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv")
Q5.How good is this gene feature in predicting y_i?
We can do this in many ways one is by simple plotting and other by simple models.
Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well.
finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0')
finalFeatureHashedFeaturesOfGene.head()
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0
1
2
3
4
5
6
7
8
Class
0
0.0
2.0
0.0
0.0
1.0
0.0
0.0
0.0
-1.0
1
1
0.0
0.0
-1.0
-1.0
0.0
0.0
-1.0
0.0
0.0
2
2
0.0
0.0
-1.0
-1.0
0.0
0.0
-1.0
0.0
0.0
2
3
0.0
0.0
-1.0
-1.0
0.0
0.0
-1.0
0.0
0.0
3
4
0.0
0.0
-1.0
-1.0
0.0
0.0
-1.0
0.0
0.0
4
finalFeatureHashedFeaturesOfGene.shape
(3321, 10)
finalFeatureHashedFeaturesOfGene.Class.value_counts()
7 953
4 686
1 568
2 452
6 275
5 242
3 89
9 37
8 19
Name: Class, dtype: int64
y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values
X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]]
model to test
def SVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM(X_Hashed,y_true_Hashed)
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
by seeing the above plot we take alpha as 0.001
def SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test))
SVMModel(X_Hashed,y_true_Hashed)
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logg loss for training data with best aplha 0.001 is [1.7364535644919004]
The Logg loss for cv data with best aplha 0.001 is [1.7737982623403665]
The Logg loss for test data with best aplha 0.001 is [1.7470137046208585]
we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
lets go to meanresponse coding features and apply SVM
finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0')
finalMeanResponseVectorsOfGene.columns
Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object')
x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']]
y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values
def SVM_meanResponseCoding(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse)
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
We ca see that i can take c = 1 from above graph Lets go for testing on test data
def SVM_meanResponseCoding_test(var1,var2):
"""
This function is use to build n test SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 =calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse)
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logg loss for training data with best aplha 1 is [1.1284981651581154]
The Logg loss for cv data with best aplha 1 is [1.1436551747978385]
The Logg loss for test data with best aplha 1 is [1.1082205650636348]
we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well.
comming to Onehotencodded Features we can use logistic regression due to high dimentions
finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0")
finalOneHotFeatures.columns
Index(['ABL1', 'ACVR1', 'AGO2', 'AKT1', 'AKT2', 'AKT3', 'ALK', 'APC', 'AR',
'ARAF',
...
'TSC2', 'U2AF1', 'VEGFA', 'VHL', 'WHSC1', 'WHSC1L1', 'XPO1', 'XRCC2',
'YAP1', 'Class'],
dtype='object', length=265)
x_oneHot=finalOneHotFeatures.iloc[:,0:264 ]
y_true_oneHot=finalOneHotFeatures.Class.values
Model
def LogisticReg_tune(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = []
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ]
for i in c:
clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
LogisticReg_tune(x_oneHot,y_true_oneHot)
(2124, 264) (2124,)
(532, 264) (532,)
(665, 264) (665,)
by seeing the above graph i can take log loss as 0.1 .
def LogisticReg_test(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = [ ]
logLoss_test=[ ]
clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 = calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test))
LogisticReg_test(x_oneHot,y_true_oneHot)
(2124, 264) (2124,)
(532, 264) (532,)
(665, 264) (665,)
The Logg loss for training data with best aplha 0.1 is [1.2638620900916107]
The Logg loss for cv data with best aplha 0.1 is [1.3524898055298527]
The Logg loss for test data with best aplha 0.1 is [1.3393455550993199]
we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well.
Observations on Gene Featurisations
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470])
table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082])
table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393])
print(table)
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
| Gene | FeatureHaser | SVM-kernel-RBF | 0.001 | 1.7364 | 1.7737 | 1.747 |
| Gene | MeanResponseCoding | SVM-kernel-RBF | 1 | 1.1284 | 1.1436 | 1.1082 |
| Gene | OneHotEncoding | LogisticRegression | 0.1 | 1.2638 | 1.3524 | 1.3393 |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
SVM with less dimensions with meanResponseCoding worked well
because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel.
Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model.
Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.3 Analysing of Variation Feature</p>
</div>
</div>
</div>
df.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
Q1. what is the Variation Feature Type?
</div>
</div>
</div>
It is Categorical
</div>
</div>
</div>
Q2.How many categories are present?
</div>
</div>
</div>
len(df.Variation.value_counts())
2996
There are 2996 categories
</div>
</div>
</div>
Q3.What is the distribution of categories?
</div>
</div>
</div>
unique_variations=df.Variation.value_counts()
sum0f_unique=sum(unique_variations)
histoGram_variation=unique_variations.values / sum0f_unique
plt.plot(histoGram_variation ,label = "Histogram of Variation")
plt.xlabel(" index of Variation")
plt.ylabel(" count")
plt.legend()
plt.grid()
plt.show()
by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0.
cumsum_histogram=np.cumsum(histoGram_variation)
plt.plot(cumsum_histogram,label='Cumulative distribution of variations')
plt.grid()
plt.legend()
plt.show()
Q4.How to Featurise this variation Feature?
</div>
</div>
</div>
There are few ways:-
1.OneHotEncoding
2.FeatureHasher
3.MeanResponseCoding
</div>
</div>
</div>
Lets Start with OneHotEncoding of VariationFeature
ds_oneHotencoding=df
ds_meanResponse_coding=df
ds_featureHasher=df
ds_oneHotencoding.columns
Index(['ID', 'Gene', 'Variation', 'Class', 'Cleaned_text'], dtype='object')
ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text'])
ds_oneHotencoding.head()
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Variation
0
Truncating Mutations
1
W802*
2
Q249E
3
N454D
4
L399V
label_encoder=LabelEncoder()
ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"])
ds_oneHotencoding
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Variation
Labelencodedvalues
0
Truncating Mutations
2629
1
W802*
2856
2
Q249E
1897
3
N454D
1667
4
L399V
1447
...
...
...
3316
D171N
306
3317
A122*
28
3318
Fusions
807
3319
R80C
2249
3320
K83E
1333
3321 rows × 2 columns
featurenames=label_encoder.classes_
ds_oneHotencoding
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Variation
Labelencodedvalues
0
Truncating Mutations
2629
1
W802*
2856
2
Q249E
1897
3
N454D
1667
4
L399V
1447
...
...
...
3316
D171N
306
3317
A122*
28
3318
Fusions
807
3319
R80C
2249
3320
K83E
1333
3321 rows × 2 columns
onehotencoder_Variation=OneHotEncoder()
encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray()
encoded_array
array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]])
ds_dash=pd.DataFrame(encoded_array , columns=featurenames)
ds_dash.head()
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1_2009trunc
2010_2471trunc
256_286trunc
3' Deletion
385_418del
422_605trunc
533_534del
534_536del
550_592del
560_561insER
596_619splice
963_D1010splice
981_1028splice
A1020V
A1022E
A1065T
A1066V
A1099T
A111P
A1131T
A113_splice
A1170V
A11_G12insGA
A1200V
A120S
A121E
A121P
A121V
A122*
A1234T
A126D
A126G
A126S
A126V
A134D
A1374V
A1459P
A146T
A146V
A148T
...
Y599_D600insGLYVDFREYEY
Y599_D600insPAPQIMSTSTLISENMNIA
Y599_D600insSTDNEYFYVDFREYEY
Y62C
Y63C
Y640F
Y646C
Y646F
Y646H
Y646N
Y646S
Y647C
Y64A
Y652H
Y65C
Y68D
Y68H
Y69H
Y772_A775dup
Y791F
Y801H
Y803N
Y806C
Y823D
Y835F
Y842C
Y846C
Y849C
Y849S
Y87C
Y87N
Y901C
Y931C
Y98H
Y98N
YAP1-FAM118B Fusion
YAP1-MAMLD1 Fusion
ZC3H7B-BCOR Fusion
ZNF198-FGFR1 Fusion
p61BRAF
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5 rows × 2996 columns
OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1)
OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues'])
OneHotEncoded_ds.head()
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1_2009trunc
2010_2471trunc
256_286trunc
3' Deletion
385_418del
422_605trunc
533_534del
534_536del
550_592del
560_561insER
596_619splice
963_D1010splice
981_1028splice
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A1065T
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A1131T
A113_splice
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A121P
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A1234T
A126D
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A134D
A1374V
A1459P
A146T
A146V
A148T
...
Y599_D600insPAPQIMSTSTLISENMNIA
Y599_D600insSTDNEYFYVDFREYEY
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Y842C
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Y849S
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YAP1-FAM118B Fusion
YAP1-MAMLD1 Fusion
ZC3H7B-BCOR Fusion
ZNF198-FGFR1 Fusion
p61BRAF
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5 rows × 2997 columns
OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv")
OneHotEncoded_ds
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1_2009trunc
2010_2471trunc
256_286trunc
3' Deletion
385_418del
422_605trunc
533_534del
534_536del
550_592del
560_561insER
596_619splice
963_D1010splice
981_1028splice
A1020V
A1022E
A1065T
A1066V
A1099T
A111P
A1131T
A113_splice
A1170V
A11_G12insGA
A1200V
A120S
A121E
A121P
A121V
A122*
A1234T
A126D
A126G
A126S
A126V
A134D
A1374V
A1459P
A146T
A146V
A148T
...
Y599_D600insPAPQIMSTSTLISENMNIA
Y599_D600insSTDNEYFYVDFREYEY
Y62C
Y63C
Y640F
Y646C
Y646F
Y646H
Y646N
Y646S
Y647C
Y64A
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Y791F
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Y806C
Y823D
Y835F
Y842C
Y846C
Y849C
Y849S
Y87C
Y87N
Y901C
Y931C
Y98H
Y98N
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YAP1-MAMLD1 Fusion
ZC3H7B-BCOR Fusion
ZNF198-FGFR1 Fusion
p61BRAF
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3321 rows × 2997 columns
Lets create feature hasher for variation features
ds_featureHasher.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"])
ds_featureHasher.head()
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Variation
Class
0
Truncating Mutations
1
1
W802*
2
2
Q249E
2
3
N454D
3
4
L399V
4
hasher= FeatureHasher(n_features=9,input_type='string')
array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray()
ds_dash_hashed=pd.DataFrame(array_hashed)
hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1)
hashedEncodedFeatureof_variation.head()
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0
1
2
3
4
5
6
7
8
Class
0
-1.0
-1.0
0.0
-8.0
5.0
0.0
0.0
0.0
1.0
1
1
1.0
0.0
0.0
-1.0
0.0
-1.0
0.0
-1.0
1.0
2
2
2.0
1.0
1.0
0.0
0.0
-1.0
0.0
0.0
0.0
2
3
0.0
0.0
0.0
1.0
0.0
-2.0
0.0
0.0
0.0
3
4
2.0
0.0
2.0
0.0
0.0
0.0
-1.0
0.0
0.0
4
hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv")
Lets go for mean responsecoding of variation feature
df_mean_response_coding.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
objects_ThisFeature=df_mean_response_coding.Variation.value_counts()
objects_ThisFeature
Truncating Mutations 93
Deletion 74
Amplification 71
Fusions 34
Overexpression 6
..
K78A 1
A1789S 1
T401I 1
K550_V555delinsI 1
R420H 1
Name: Variation, Length: 2996, dtype: int64
my_dictionary_varFeature={ }
for feature_name , feature_total_count in objects_ThisFeature.items():
vector_array_features=[ ]
for index in range(1,10):
count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)])
vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90)))
my_dictionary_varFeature[feature_name] = vector_array_features
my_dictionary_varFeature
{'Truncating Mutations': [0.5409836065573771,
0.060109289617486336,
0.0546448087431694,
0.060109289617486336,
0.0546448087431694,
0.06557377049180328,
0.0546448087431694,
0.0546448087431694,
0.0546448087431694],
'Deletion': [0.40853658536585363,
0.06097560975609756,
0.06097560975609756,
0.15853658536585366,
0.06097560975609756,
0.06707317073170732,
0.06097560975609756,
0.06097560975609756,
0.06097560975609756],
'Amplification': [0.062111801242236024,
0.19254658385093168,
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0.3105590062111801,
0.062111801242236024,
0.062111801242236024],
'Fusions': [0.0967741935483871,
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0.08064516129032258,
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0.08064516129032258,
0.08870967741935484,
0.08064516129032258],
'Overexpression': [0.10416666666666667,
0.125,
0.10416666666666667,
0.10416666666666667,
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0.10416666666666667],
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'Promoter Hypermethylation': [0.10869565217391304,
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'TMPRSS2-ETV1 Fusion': [0.10869565217391304,
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'V1075F': [0.10989010989010989,
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'S23R': [0.10989010989010989,
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0.12087912087912088,
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'F1592S': [0.10989010989010989,
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'T783A': [0.10989010989010989,
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'Y599_D600insPAPQIMSTSTLISENMNIA': [0.10989010989010989,
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0.12087912087912088,
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'S645C': [0.10989010989010989,
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0.10989010989010989,
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'Y236C': [0.10989010989010989,
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0.12087912087912088,
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'D1010N': [0.10989010989010989,
0.12087912087912088,
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0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989],
'N387K': [0.10989010989010989,
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0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
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0.12087912087912088,
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0.10989010989010989],
'K111N': [0.10989010989010989,
0.12087912087912088,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989],
'D422N': [0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.12087912087912088,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989,
0.10989010989010989],
...}
length_df=len(df_mean_response_coding)
array_of_vectorss=[ ]
for i in range(0,length_df):
if df_mean_response_coding.iloc[i]['Variation'] in my_dictionary_varFeature.keys():
featurename=df_mean_response_coding.iloc[i]['Variation']
array_of_vectorss.append( my_dictionary_varFeature[featurename] )
else:
array_of_vectorss.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
print(array_of_vectorss)
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0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 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0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.062111801242236024, 0.19254658385093168, 0.062111801242236024, 0.062111801242236024, 0.062111801242236024, 0.12422360248447205, 0.3105590062111801, 0.062111801242236024, 0.062111801242236024], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989]]
meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss)
meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1)
meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv")
As we have all the vector forms of the featurisations lets proceed with further analysis
Q5.How good is this Variation feature in predicting y_i?
Lets Build simple models to check our selves
As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library.
1.meanResponse coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
SVM Model
meanResponseCoding_variationFeature.columns
Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 'Class'], dtype='object')
x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values
y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values
x_true_meanresponse
array([[0.54098361, 0.06010929, 0.05464481, ..., 0.05464481, 0.05464481,
0.05464481],
[0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011,
0.10989011],
[0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011,
0.10989011],
...,
[0.09677419, 0.33064516, 0.08064516, ..., 0.08064516, 0.08870968,
0.08064516],
[0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011,
0.10989011],
[0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011,
0.10989011]])
def svm_model_tuning(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
svm_model_tuning(x_true_meanresponse , y_true_meanResponse)
The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
From above graph i select my best C as 1
Testing of the model
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
svm_model_testing(x_true_meanresponse,y_true_meanResponse)
The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logloss for 1 the coresponding train loss is [0.13402600125222605]
The Logloss for 1 the coresponding cv loss is [0.1479095316786708]
The Logloss for 1 the coresponding test loss is [0.12267536305037746]
we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
2.Hashed coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0')
hashedEncodedFeatureof_variation.head()
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0
1
2
3
4
5
6
7
8
Class
0
-1.0
-1.0
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x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values
y_hashed_true=hashedEncodedFeatureof_variation['Class'].values
We will reuse the same svm functions we defiend earilier
svm_model_tuning(x_hashed_true,y_hashed_true)
The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
from the above graph i can choose my C as 0.0001
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test ))
svm_model_testing(x_hashed_true,y_hashed_true)
The shape of the train n test vector as follows:
(2124, 9) (2124,)
(532, 9) (532,)
(665, 9) (665,)
The Logloss for 0.0001 the coresponding train loss is [1.7335822874941234]
The Logloss for 0.0001 the coresponding cv loss is [1.753463388538884]
The Logloss for 0.0001 the coresponding test loss is [1.7599055604165221]
we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
3.OneHotencoding of variation feature
As this has high dimention lets use logistic regression
</div>
</div>
</div>
oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0')
oneHotEncodedfeaturesof_Variation.head()
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1_2009trunc
2010_2471trunc
256_286trunc
3' Deletion
385_418del
422_605trunc
533_534del
534_536del
550_592del
560_561insER
596_619splice
963_D1010splice
981_1028splice
A1020V
A1022E
A1065T
A1066V
A1099T
A111P
A1131T
A113_splice
A1170V
A11_G12insGA
A1200V
A120S
A121E
A121P
A121V
A122*
A1234T
A126D
A126G
A126S
A126V
A134D
A1374V
A1459P
A146T
A146V
A148T
...
Y599_D600insPAPQIMSTSTLISENMNIA
Y599_D600insSTDNEYFYVDFREYEY
Y62C
Y63C
Y640F
Y646C
Y646F
Y646H
Y646N
Y646S
Y647C
Y64A
Y652H
Y65C
Y68D
Y68H
Y69H
Y772_A775dup
Y791F
Y801H
Y803N
Y806C
Y823D
Y835F
Y842C
Y846C
Y849C
Y849S
Y87C
Y87N
Y901C
Y931C
Y98H
Y98N
YAP1-FAM118B Fusion
YAP1-MAMLD1 Fusion
ZC3H7B-BCOR Fusion
ZNF198-FGFR1 Fusion
p61BRAF
Class
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4
5 rows × 2997 columns
oneHotEncodedfeaturesof_Variation.columns
Index(['1_2009trunc', '2010_2471trunc', '256_286trunc', '3' Deletion',
'385_418del', '422_605trunc', '533_534del', '534_536del', '550_592del',
'560_561insER',
...
'Y901C', 'Y931C', 'Y98H', 'Y98N', 'YAP1-FAM118B Fusion',
'YAP1-MAMLD1 Fusion', 'ZC3H7B-BCOR Fusion', 'ZNF198-FGFR1 Fusion',
'p61BRAF', 'Class'],
dtype='object', length=2997)
x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values
y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values
LogisticModel
def logistic_tune(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
logistic_tune(x_onehot_true,y_onehot_true)
The shape of the train n test vector as follows:
(2124, 2996) (2124,)
(532, 2996) (532,)
(665, 2996) (665,)
From the above graph i can choos aplha as 0.01
Testing model
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test ))
logistic_test(x_onehot_true,y_onehot_true)
The shape of the train n test vector as follows:
(2124, 2996) (2124,)
(532, 2996) (532,)
(665, 2996) (665,)
The Logloss for 0.01 the coresponding train loss is [1.4238762684713966]
The Logloss for 0.01 the coresponding cv loss is [1.744474382727282]
The Logloss for 0.01 the coresponding test loss is [1.7380242209450858]
we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets compare all of them
Lets compare all observations on Variation Feature
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599])
table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479])
table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380])
print(table)
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
| Variation | FeatureHaser | SVM-kernel-RBF | 0.0001 | 1.7335 | 1.7534 | 1.7599 |
| Variation | MeanResponseCoding | SVM-kernel-RBF | 1 | 0.134 | 0.1479 | 0.1479 |
| Variation | OneHotEncoding | LogisticRegression | 0.01 | 1.4238 | 1.7444 | 1.738 |
+--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+
From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well.
Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.4 Analysing of Text Feature</p>
</div>
</div>
</div>
Q1.What kind of feature is Text
</div>
</div>
</div>
It is sentance corpus consisting of many sentances.
Q2.how can we featurise it
There are many ways we will work with these two
1.BOW
2.TFIDF
</div>
</div>
</div>
1.Featurising with bow
df.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
bow_df=df
bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])
array_input=bow_df.Cleaned_text.values
bow_ds=df
vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)
tf=vectoriser.fit(array_input)
vocab_countVectoriser=(tf.vocabulary_)
vocab_countVectoriser.items()
dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 4745), ('without', 4963), ('partner', 3273), ('knowledge', 2441), ('gap', 1856), ('largely', 2467), ('biological', 458), ('act', 65), ('positive', 3470), ('cell', 650), ('cycle', 1072), ('regulator', 3830), ('tumor', 4708), ('suppressor', 4461), ('others', 3219), ('interacts', 2321), ('transcription', 4626), ('factor', 1645), ('inhibits', 2279), ('transcriptional', 4631), ('unknown', 4818), ('knockdown', 2439), ('expression', 1612), ('activates', 73), ('induces', 2258), ('mcf', 2702), ('binding', 439), ('gene', 1884), ('predict', 3504), ('absence', 22), ('truncation', 4698), ('heterozygous', 2053), ('females', 1674), ('pathogenesis', 3279), ('recombinant', 3789), ('heterodimer', 2046), ('active', 85), ('phospho', 3377), ('erk', 1493), ('inducing', 2260), ('estrogen', 1514), ('receptor', 3780), ('er', 1485), ('phosphorylation', 3386), ('finally', 1730), ('detect', 1192), ('level', 2508), ('observed', 3161), ('yeast', 4989), ('hybrid', 2124), ('screen', 4048), 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'cell transformation',
'cell type',
'cell types',
'cell viability',
'cells',
'cells analyzed',
'cells cell',
'cells cells',
'cells co',
'cells compared',
'cells cultured',
'cells data',
'cells express',
'cells expressing',
'cells fig',
'cells figure',
'cells grown',
'cells harboring',
'cells harvested',
'cells incubated',
'cells infected',
'cells lysed',
'cells maintained',
'cells per',
'cells plated',
'cells seeded',
'cells showed',
'cells stably',
'cells transduced',
'cells transfected',
'cells transiently',
'cells treated',
'cells used',
'cells using',
'cells washed',
'cells well',
'cellular',
'cellular proliferation',
'center',
'central',
'centrifugation',
'centrifuged',
'centrosome',
'cerevisiae',
'ceritinib',
'certain',
'cervical',
'cetuximab',
'cfc',
'cfc syndrome',
'cgh',
'chain',
'chain reaction',
'chains',
'challenge',
'change',
'change structure',
'changed',
'changes',
'characteristic',
'characteristics',
'characterization',
'characterize',
'characterized',
'charge',
'charged',
'checkpoint',
'chek',
'chemical',
'chemotherapy',
'chen',
'chen et',
'childhood',
'children',
'chimeric',
'chip',
'chk',
'cho',
'chosen',
'chromatin',
'chromatography',
'chromosomal',
'chromosome',
'chromosomes',
'chronic',
'ci',
'cic',
'cient',
'cis',
'cisplatin',
'cisplatin sensitivity',
'city',
'ck',
'cl',
'class',
'class variants',
'classes',
'classical',
'classifi',
'classifi cation',
'classifi ed',
'classification',
'classifications',
'classified',
'classify',
'clear',
'clear cell',
'clearly',
'cleavage',
'cleft',
'clin',
'clinic',
'clinical',
'clinical benefit',
'clinical characteristics',
'clinical data',
'clinical features',
'clinical information',
'clinical outcome',
'clinical relevance',
'clinical response',
'clinical significance',
'clinical trial',
'clinical trials',
'clinically',
'clinically relevant',
'cll',
'clonal',
'clone',
'cloned',
'clones',
'cloning',
'clontech',
'close',
'closed',
'closely',
'cluster',
'clustered',
'clustering',
'clusters',
'cm',
'cml',
'cmml',
'cmv',
'cns',
'co',
'co expressed',
'co expression',
'co occurrence',
'co transfected',
'coactivator',
'code',
'coding',
'coding region',
'coding sequence',
'codon',
'codons',
'coexpressed',
'coexpression',
'cohort',
'cohorts',
'coil',
'coiled',
'coiled coil',
'cold',
'coli',
'collagen',
'colleagues',
'collected',
'collection',
'collectively',
'colon',
'colon cancer',
'colonies',
'colony',
'colony formation',
'color',
'colorectal',
'colorectal cancer',
'colorectal cancers',
'colored',
'column',
'columns',
'combination',
'combinations',
'combined',
'combining',
'committee',
'common',
'commonly',
'communication',
'comparable',
'comparative',
'compare',
'compared',
'compared cells',
'compared control',
'compared wild',
'compared wt',
'comparing',
'comparison',
'comparisons',
'competent',
'competitive',
'complement',
'complementary',
'complementation',
'complete',
'complete loss',
'completely',
'complex',
'complex formation',
'complexes',
'component',
'components',
'composed',
'compound',
'compounds',
'comprehensive',
'comprise',
'comprised',
'comprises',
'comprising',
'compromised',
'computational',
'computed',
'concentration',
'concentrations',
'concept',
'conclude',
'concluded',
'conclusion',
'conclusions',
'concomitant',
'concordance',
'concurrent',
'condition',
'conditional',
'conditions',
'conducted',
'confer',
'confer resistance',
'conferred',
'conferring',
'confers',
'confidence',
'confirm',
'confirmation',
'confirmed',
'confirming',
'confluence',
'conformation',
'conformational',
'conformational change',
'conformational changes',
'conformations',
'congenital',
'conjugated',
'consecutive',
'consensus',
'consent',
'consequence',
'consequences',
'consequently',
'conservation',
'conservative',
'conserved',
'consider',
'considerable',
'considered',
'considering',
'consisted',
'consistent',
'consistent previous',
'consistently',
'consisting',
'consists',
'consortium',
'constant',
'constitute',
'constitutional',
'constitutive',
'constitutive activation',
'constitutively',
'constitutively activated',
'constitutively active',
'construct',
'constructed',
'construction',
'constructs',
'contact',
'contact help',
'contacts',
'contain',
'contained',
'containing',
'containing mm',
'contains',
'content',
'context',
'contexts',
'continued',
'continuous',
'contrast',
'contribute',
'contributed',
'contributes',
'contributing',
'contribution',
'control',
'control cells',
'controlled',
'controlling',
'controls',
'conventional',
'conversely',
'conversion',
'cooh',
'cooh terminal',
'cooperate',
'cooperative',
'copies',
'copy',
'copy number',
'core',
'core domain',
'core enzyme',
'correct',
...]
array_transformed=vectoriser.transform(array_input)
array_transformedone=array_transformed.toarray()
dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )
dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)
dataframofBowForText.head()
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}
aa
aacrjournals
aacrjournals org
ab
abbreviations
abc
abd
aberrant
aberrant splicing
aberrations
abi
ability
ability bind
ability induce
abl
able
abnormal
abnormalities
abolish
abolished
abrogate
abrogated
absence
absent
absolute
abstract
abundance
abundant
ac
acc
accelerated
accepted
acceptor
access
access image
accessible
accessible alternative
accession
accompanied
accordance
...
withdrawal
within
without
wm
wm cells
wnt
women
work
worldwide
worse
wpd
written
wt
wt mutant
wt wt
wtb
wtb raf
wu
xenograft
xenografts
xenopus
xl
xp
xrcc
xu
yap
year
years
yeast
yeast cells
yellow
yes
yet
yield
yielded
young
zhang
zinc
zn
Class
0
0
0
0
1
0
0
0
2
1
0
0
1
0
0
0
0
0
2
0
0
0
0
3
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
1
...
0
3
3
0
0
0
0
2
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
2
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
2
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
...
0
1
3
0
0
0
2
0
0
1
0
2
6
0
0
0
0
0
0
0
0
1
0
0
0
0
2
1
0
0
0
0
3
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
2
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
...
0
1
3
0
0
0
2
0
0
1
0
2
6
0
0
0
0
0
0
0
0
1
0
0
0
0
2
1
0
0
0
0
3
0
0
0
0
0
0
2
3
0
0
0
0
1
0
0
2
0
0
1
1
0
0
3
2
0
8
0
1
0
1
4
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
...
2
2
5
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
3
4
0
0
0
1
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
...
0
4
2
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
10
4
5 rows × 5001 columns
dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")
2.TFIDF featurisation of text data
</div>
</div>
</div>
df.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
tfidf_df=df
tfidf_ds=df
tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])
tfidf_df
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Cleaned_text
0
cyclin dependent kinases cdks regulate variety...
1
abstract background non small cell lung cancer...
2
abstract background non small cell lung cancer...
3
recent evidence demonstrated acquired uniparen...
4
oncogenic mutations monomeric casitas b lineag...
...
...
3316
introduction myelodysplastic syndromes mds het...
3317
introduction myelodysplastic syndromes mds het...
3318
runt related transcription factor gene runx al...
3319
runx gene frequent target chromosomal transloc...
3320
frequent mutations associated leukemia recurre...
3321 rows × 1 columns
array_input_tfidf=tfidf_df['Cleaned_text'].values
vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)
vectorizer.fit(array_input_tfidf)
TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.float64'>, encoding='utf-8',
input='content', lowercase=True, max_df=1.0, max_features=1000,
min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None,
smooth_idf=True, stop_words=None, strip_accents=None,
sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None, use_idf=True, vocabulary=None)
len(vectorizer.vocabulary_)
1000
featurenames_tfidf=vectorizer.get_feature_names()
tfidf_vocab=vectorizer.vocabulary_
transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)
transformed_tfidf_csr=transformed_tfidf_csr.toarray()
arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)
tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)
tfidf_textfeature.to_csv("tfidf_textfeatures.csv")
tfidf_textfeature
.dataframe tbody tr th:only-of-type {
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}
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
adenocarcinoma
advanced
affect
affected
affecting
affinity
age
akt
al
ala
alk
allele
alleles
alone
alterations
altered
alternative
although
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
0.007393
0.002483
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0.000000
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0.002667
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0.003147
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0.0
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0.003635
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0.014516
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0.005902
0.047440
0.048425
0.004343
0.010854
0.000000
0.006067
0.006067
0.004734
0.006265
0.007224
0.028735
0.000000
0.017848
0.005846
1
1
0.000000
0.000000
0.007145
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0.005449
0.001641
0.000000
0.002820
0.002611
0.002394
0.002114
0.004184
0.00000
0.004967
0.0
0.000000
0.002438
0.021031
0.000000
0.009145
0.003209
0.014053
0.0
0.007465
0.002027
0.002582
0.000000
0.002612
0.002856
0.001877
0.0
0.0
0.006657
0.002446
0.006778
0.016340
0.000000
0.000000
0.004482
...
0.007657
0.002158
0.002277
0.000000
0.007354
0.034397
0.000000
0.000000
0.000000
0.000000
0.006110
0.013063
0.000000
0.000000
0.0
0.002291
0.000000
0.000000
0.000000
0.002765
0.0
0.0
0.022081
0.000000
0.008978
0.004245
0.005261
0.001651
0.003302
0.004868
0.010767
0.010767
0.003600
0.001588
0.005494
0.013112
0.002496
0.000000
0.006669
2
2
0.000000
0.000000
0.007145
0.000000
0.000000
0.005449
0.001641
0.000000
0.002820
0.002611
0.002394
0.002114
0.004184
0.00000
0.004967
0.0
0.000000
0.002438
0.021031
0.000000
0.009145
0.003209
0.014053
0.0
0.007465
0.002027
0.002582
0.000000
0.002612
0.002856
0.001877
0.0
0.0
0.006657
0.002446
0.006778
0.016340
0.000000
0.000000
0.004482
...
0.007657
0.002158
0.002277
0.000000
0.007354
0.034397
0.000000
0.000000
0.000000
0.000000
0.006110
0.013063
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0.000000
0.0
0.002291
0.000000
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0.000000
0.002765
0.0
0.0
0.022081
0.000000
0.008978
0.004245
0.005261
0.001651
0.003302
0.004868
0.010767
0.010767
0.003600
0.001588
0.005494
0.013112
0.002496
0.000000
0.006669
2
3
0.008309
0.002790
0.015838
0.006590
0.010917
0.002684
0.002426
0.005995
0.058335
0.003859
0.000000
0.006248
0.009276
0.00846
0.004893
0.0
0.000000
0.003603
0.033300
0.003567
0.002252
0.007113
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0.000000
0.026962
0.007630
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0.0
0.0
0.009838
0.018076
0.003339
0.000000
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0.017664
...
0.000000
0.012758
0.003365
0.000000
0.006521
0.027534
0.000000
0.003180
0.016809
0.025671
0.000000
0.005516
0.003996
0.000000
0.0
0.003385
0.018474
0.000000
0.000000
0.004085
0.0
0.0
0.008158
0.000000
0.013268
0.000000
0.000000
0.009762
0.017079
0.007194
0.036367
0.036367
0.002660
0.004694
0.013531
0.009689
0.007378
0.000000
0.000000
3
4
0.000000
0.003513
0.000000
0.000000
0.000000
0.020280
0.003054
0.001887
0.000000
0.000000
0.002227
0.007867
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0.024644
0.0
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0.004647
0.006950
...
0.000000
0.006024
0.008475
0.003597
0.010948
0.018668
0.017570
0.012011
0.000000
0.004040
0.003789
0.006945
0.007547
0.000000
0.0
0.004263
0.000000
0.000000
0.001934
0.000000
0.0
0.0
0.005136
0.000000
0.001392
0.000000
0.000000
0.006146
0.001536
0.009058
0.028620
0.028620
0.001675
0.005911
0.003408
0.000000
0.002323
0.000000
0.002068
4
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
3316
0.000000
0.006259
0.000000
0.003696
0.001531
0.016559
0.005441
0.000000
0.007010
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0.000000
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0.000000
0.00000
0.006860
0.0
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0.000000
0.004980
0.006001
0.010105
0.001330
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0.0
0.001547
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0.004330
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0.006224
0.0
0.0
0.000000
0.000000
0.013106
0.003385
0.000000
0.000000
0.004953
...
0.008461
0.005366
0.000000
0.000000
0.013408
0.039196
0.000000
0.001783
0.000000
0.000000
0.003375
0.012373
0.000000
0.002139
0.0
0.003797
0.002072
0.000000
0.008612
0.006873
0.0
0.0
0.004575
0.005105
0.004960
0.012312
0.010901
0.000000
0.005473
0.000000
0.008923
0.008923
0.007459
0.007898
0.009106
0.048900
0.000000
0.000000
0.000000
4
3317
0.000000
0.006038
0.000000
0.004754
0.001969
0.009682
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0.000000
0.000000
0.000000
0.000000
0.009749
0.001710
0.000000
0.0
0.001989
0.000000
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4
3321 rows × 1001 columns
Q3. Is this Bow,tfidf for text feature Useful in our classification?
</div>
</div>
</div>
Lets find out by tsne and one simple model.
TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values
y_true_std=dataframofBowForText.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)
intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Tsne for TFIDF data
x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values
y_tfidf_true=tfidf_textfeature.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Lets do analisys based on models for the two featurisations and compare the results
Lets go with simple model like logistic regression for high dimension data
1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
oneHot_Logsitic( data,y_true_std)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one was with standardised data
oneHot_Logsitic( x_std,y_true_std)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one is without standardised data
from the graphs i can take my alpha as 0.001
Testing on test datasets
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))
logistic_test(x_std,y_true_std)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [1.0724656346673704]
The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368]
The Logloss for 0.001 the coresponding test loss is [1.3034431218014317]
Above is result on normal data
logistic_test(data,y_true_std)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [0.9441582057917238]
The Logloss for 0.001 the coresponding cv loss is [1.210222073584811]
The Logloss for 0.001 the coresponding test loss is [1.1539667998430334]
Above is Result on standardised data
we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective
Lets look at other featurisations as well.
2.TFIDF feauture for text
Using Logisticmodels we bult for previous cases
Tuning
oneHot_Logsitic(x_tfidf_true,y_tfidf_true)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one was without standardised data
oneHot_Logsitic(data,y_tfidf_true)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one was with standardised data
From the graphs i can select my alpha as 0.001
Lets Test the model
without standardised data
logistic_test(x_tfidf_true,y_tfidf_true)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [1.0655639055266641]
The Logloss for 0.001 the coresponding cv loss is [1.287554414069922]
The Logloss for 0.001 the coresponding test loss is [1.2673057224719144]
with standardised data
logistic_test(data,y_tfidf_true)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [0.9062325983187661]
The Logloss for 0.001 the coresponding cv loss is [1.154476481391309]
The Logloss for 0.001 the coresponding test loss is [1.2146295572433117]
Observations of Text featureised models with Standardised data only</p>
</div>
</div>
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss |
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
| Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 |
| Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 |
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div>
</div>
</div>
Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div>
</div>
</div>
4.0 Modelling </p>
</div>
</div>
</div>
4.1 Creating Miscellenous Functions
Confusion Matrix,Precision,Recall</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object')
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid',
'acids', 'acquired', 'across',
...
'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt',
'years', 'yeast', 'yet'],
dtype='object', length=1000)
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
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0
1
2
3
4
5
6
7
8
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1
2
3
4
5
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7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
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0.396532
-0.607300
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-0.580938
3.449077
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4
3321 rows × 1019 columns
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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3321 rows × 4261 columns
3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
0
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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3321 rows × 1019 columns
Now we have our data sets lets perform modelling.
Datasets info:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model</p>
</div>
</div>
</div>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
The hyper parameter and logloss for Train data are :1 and [0.9633295236937504]
The hyper parameter and logloss for Train data are :1 and [1.1924015413394058]
The hyper parameter and logloss for Train data are :1 and [1.1982778811800479]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
This is the Base Line Model Results it performed well.
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
withoutstd_gene_var_text_meanTdidf
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acids
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across
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added
addition
additional
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university
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upon
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variants
various
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vhl
via
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4
3321 rows × 1019 columns
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
The hyper parameter and logloss for Train data are :1 and [1.0383832549539975]
The hyper parameter and logloss for Train data are :1 and [1.2612556656065605]
The hyper parameter and logloss for Train data are :1 and [1.2320081611414992]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.2 Knn Model</p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
Counter({1: 917,
2: 921,
3: 945,
4: 926,
5: 932,
6: 941,
7: 913,
8: 953,
9: 953})
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :31 and [0.813976747977174]
The hyper parameter and logloss for cv data are :31 and [0.8788933809062333]
The hyper parameter and logloss for Test data are :31 and [0.8810438075247502]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
.dataframe tbody tr th:only-of-type {
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0
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
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4
3321 rows × 1019 columns
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
balancing class using SMOTE
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Counter({1: 921,
2: 926,
3: 949,
4: 926,
5: 932,
6: 944,
7: 919,
8: 953,
9: 953})
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :31 and [0.6814415244209885]
The hyper parameter and logloss for cv data are :31 and [0.7016829214824364]
The hyper parameter and logloss for Test data are :31 and [0.7779077172643827]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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...
...
...
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...
...
...
...
...
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...
...
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4
3321 rows × 1019 columns
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
Balancing using smote
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Counter({1: 915,
2: 916,
3: 944,
4: 923,
5: 924,
6: 938,
7: 907,
8: 953,
9: 953})
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :15 and [0.6474458212209986]
The hyper parameter and logloss for cv data are :15 and [0.7512635680512498]
The hyper parameter and logloss for Test data are :31 and [0.7368556387957963]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.3 Logistic Regression</p>
</div>
</div>
</div>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
The shape of the train n test vector as follows:
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logloss for 1 the coresponding train loss is [0.7850156568089277]
The Logloss for 1 the coresponding cv loss is [1.1961412442201744]
The Logloss for 1 the coresponding test loss is [1.1748189424714095]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
.dataframe tbody tr th:only-of-type {
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0
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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0.164191
4
3321 rows × 1019 columns
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.5847329559769139]
The Logloss for 1 the coresponding cv loss is [0.8572068041315724]
The Logloss for 1 the coresponding test loss is [0.8214264881831812]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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...
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...
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...
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0.164191
4
3321 rows × 1019 columns
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.8735667598121121]
The Logloss for 1 the coresponding cv loss is [1.1928582990002885]
The Logloss for 1 the coresponding test loss is [1.1999154339757783]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.4 SVM </p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
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ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
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0.0
0.0
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4
3321 rows × 4261 columns
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logg loss for training data with best aplha 0.1 is [0.9361514695402671]
The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932]
The Logg loss for test data with best aplha 0.1 is [1.1689839246304874]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.8782130606666145]
The Logg loss for cv data with best aplha 1 is [1.104589994061247]
The Logg loss for test data with best aplha 1 is [1.0574913033666131]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
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...
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4
3321 rows × 1019 columns
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.9725367454691902]
The Logg loss for cv data with best aplha 1 is [1.1331135752614927]
The Logg loss for test data with best aplha 1 is [1.2037472465904475]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.5 Random Forest</p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 4261 columns
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 1.2606812233244562
Log Loss train: 1.1374431015135666
for n_estimators = 100 and max depth = 5
Log Loss cv: 1.1653337133462423
Log Loss train: 0.9371070459846149
for n_estimators = 100 and max depth = 7
Log Loss cv: 1.12209548509024
Log Loss train: 0.7363291411007856
for n_estimators = 100 and max depth = 10
Log Loss cv: 1.10268480641603
Log Loss train: 0.5519688514792611
for n_estimators = 200 and max depth = 3
Log Loss cv: 1.248631824458489
Log Loss train: 1.1184306206599595
for n_estimators = 200 and max depth = 5
Log Loss cv: 1.1564275473529837
Log Loss train: 0.919144190096995
for n_estimators = 200 and max depth = 7
Log Loss cv: 1.110794380130624
Log Loss train: 0.7207481407448507
for n_estimators = 200 and max depth = 10
Log Loss cv: 1.0961422234706304
Log Loss train: 0.5423272076360501
for n_estimators = 500 and max depth = 3
Log Loss cv: 1.2478678013942157
Log Loss train: 1.114370022235754
for n_estimators = 500 and max depth = 5
Log Loss cv: 1.148009893446
Log Loss train: 0.9050771353940641
for n_estimators = 500 and max depth = 7
Log Loss cv: 1.1026865237241201
Log Loss train: 0.7101699614090803
for n_estimators = 500 and max depth = 10
Log Loss cv: 1.0917480510399207
Log Loss train: 0.5373319633612905
for n_estimators = 1000 and max depth = 3
Log Loss cv: 1.2414453238075647
Log Loss train: 1.1053160288591364
for n_estimators = 1000 and max depth = 5
Log Loss cv: 1.1452379981760221
Log Loss train: 0.900926228080795
for n_estimators = 1000 and max depth = 7
Log Loss cv: 1.0990520521977565
Log Loss train: 0.7050443392267894
for n_estimators = 1000 and max depth = 10
Log Loss cv: 1.0908700848502797
Log Loss train: 0.5357777692154363
for n_estimators = 2000 and max depth = 3
Log Loss cv: 1.240682648359384
Log Loss train: 1.1036497793577018
for n_estimators = 2000 and max depth = 5
Log Loss cv: 1.1411916861993632
Log Loss train: 0.8950347894321122
for n_estimators = 2000 and max depth = 7
Log Loss cv: 1.0971090158549015
Log Loss train: 0.7018660431272183
for n_estimators = 2000 and max depth = 10
Log Loss cv: 1.0925882642141658
Log Loss train: 0.5355792048858152
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
Log Loss cv: 1.11768909971263
Log Loss train: 0.9002694349418067
Log Loss test: 1.1477524391499037
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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0
1
2
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7
8
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1
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8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 0.23570046546599333
Log Loss train: 0.19363398591104833
for n_estimators = 100 and max depth = 5
Log Loss cv: 0.22955561081586118
Log Loss train: 0.16166542392105412
for n_estimators = 100 and max depth = 7
Log Loss cv: 0.23627364969006484
Log Loss train: 0.13622754279491436
for n_estimators = 100 and max depth = 10
Log Loss cv: 0.23299169854143326
Log Loss train: 0.09219438260645411
for n_estimators = 200 and max depth = 3
Log Loss cv: 0.15347165131328178
Log Loss train: 0.1289817833963096
for n_estimators = 200 and max depth = 5
Log Loss cv: 0.18867692478728829
Log Loss train: 0.1333800470656138
for n_estimators = 200 and max depth = 7
Log Loss cv: 0.19812746725522537
Log Loss train: 0.11363125813131297
for n_estimators = 200 and max depth = 10
Log Loss cv: 0.2073843284191654
Log Loss train: 0.08264026371353654
for n_estimators = 500 and max depth = 3
Log Loss cv: 0.14838448934316534
Log Loss train: 0.12796172100789224
for n_estimators = 500 and max depth = 5
Log Loss cv: 0.1732893968732289
Log Loss train: 0.1257528101073332
for n_estimators = 500 and max depth = 7
Log Loss cv: 0.18502788808193749
Log Loss train: 0.1059774981029388
for n_estimators = 500 and max depth = 10
Log Loss cv: 0.20316074184161176
Log Loss train: 0.0820099522892585
for n_estimators = 1000 and max depth = 3
Log Loss cv: 0.15096173695782245
Log Loss train: 0.13021826710995227
for n_estimators = 1000 and max depth = 5
Log Loss cv: 0.17583691544250576
Log Loss train: 0.12659716596839768
for n_estimators = 1000 and max depth = 7
Log Loss cv: 0.1862603932648018
Log Loss train: 0.10603952186939303
for n_estimators = 1000 and max depth = 10
Log Loss cv: 0.20453842968817895
Log Loss train: 0.08168228699376241
for n_estimators = 2000 and max depth = 3
Log Loss cv: 0.14749154103551826
Log Loss train: 0.1272450725093758
for n_estimators = 2000 and max depth = 5
Log Loss cv: 0.16918496782525078
Log Loss train: 0.12115201697127558
for n_estimators = 2000 and max depth = 7
Log Loss cv: 0.1820690821751895
Log Loss train: 0.10282294620866399
for n_estimators = 2000 and max depth = 10
Log Loss cv: 0.1984320940418906
Log Loss train: 0.07940530139460128
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 0.16118368189224744
Log Loss train: 0.1295171475728342
Log Loss test: 0.17283674779043046
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 1.2562525882383961
Log Loss train: 1.1495093667153717
for n_estimators = 100 and max depth = 5
Log Loss cv: 1.1414141205937414
Log Loss train: 0.9137821932641781
for n_estimators = 100 and max depth = 7
Log Loss cv: 1.0891671228800952
Log Loss train: 0.7274585518989484
for n_estimators = 100 and max depth = 10
Log Loss cv: 1.0602735362314917
Log Loss train: 0.5419761320891583
for n_estimators = 200 and max depth = 3
Log Loss cv: 1.2256031707183694
Log Loss train: 1.1125457127985048
for n_estimators = 200 and max depth = 5
Log Loss cv: 1.1239819425283584
Log Loss train: 0.8863029989503483
for n_estimators = 200 and max depth = 7
Log Loss cv: 1.0779197694234626
Log Loss train: 0.7025060768309693
for n_estimators = 200 and max depth = 10
Log Loss cv: 1.0544180795539924
Log Loss train: 0.5270883620171445
for n_estimators = 500 and max depth = 3
Log Loss cv: 1.207676966917158
Log Loss train: 1.1036352816983548
for n_estimators = 500 and max depth = 5
Log Loss cv: 1.1147682551650802
Log Loss train: 0.8732109339573471
for n_estimators = 500 and max depth = 7
Log Loss cv: 1.0736966943880142
Log Loss train: 0.6951080822600649
for n_estimators = 500 and max depth = 10
Log Loss cv: 1.0509723046279644
Log Loss train: 0.522813716084056
for n_estimators = 1000 and max depth = 3
Log Loss cv: 1.203560840318964
Log Loss train: 1.100936649316696
for n_estimators = 1000 and max depth = 5
Log Loss cv: 1.1104963322013828
Log Loss train: 0.8689259823733123
for n_estimators = 1000 and max depth = 7
Log Loss cv: 1.0701797377723286
Log Loss train: 0.6895920716141755
for n_estimators = 1000 and max depth = 10
Log Loss cv: 1.0502619913750308
Log Loss train: 0.5205027839317523
for n_estimators = 2000 and max depth = 3
Log Loss cv: 1.2024191406698308
Log Loss train: 1.1001196279920487
for n_estimators = 2000 and max depth = 5
Log Loss cv: 1.1083923280407713
Log Loss train: 0.8684530548880646
for n_estimators = 2000 and max depth = 7
Log Loss cv: 1.0670849255877433
Log Loss train: 0.687383236130885
for n_estimators = 2000 and max depth = 10
Log Loss cv: 1.049339910790633
Log Loss train: 0.518731457270126
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 1.12511787673414
Log Loss train: 0.8844410482107985
Log Loss test: 1.1406196373627946
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.6 Lets apply Stacking classifier</p>
</div>
</div>
</div>
Applying Stacking Classifer on MeanResponse Coding
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)
Log loss (train) on the stacking classifier : 0.14030289465971651
Log loss (cv) on the stacking classifier : 0.2420519229697048
Log loss (train) on the stacking classifier : 0.25972844103712256
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)
for meta classifier i can take my c values as 0.1
Applyin stacking for onehotfeatures
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-4ee702f64944> in <module>()
----> 1 stackingClassifier_tunning(xres,yres)
NameError: name 'stackingClassifier_tunning' is not defined
taking c as 1
stackingClassifier(xres,yres)
(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.30903590626810395
Log loss (cv) on the stacking classifier : 0.5317070749588001
Log loss (train) on the stacking classifier : 0.5271830152079766
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
taking c as 0.1
stackingClassifier(xres,yres)
(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.31039276656264053
Log loss (cv) on the stacking classifier : 0.5340512149631327
Log loss (train) on the stacking classifier : 0.5269476489328632
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
Applyin stacking for hashed Featurisation
std_gene_var_text_hashingtfidf
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vertical-align: top;
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
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according
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activate
activated
activating
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activation
activation loop
active
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weeks
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whereas
whether
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years
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yet
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3321 rows × 1019 columns
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
stackingClassifier_tunning(xres,yres)
(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)
Log loss (train) on the stacking classifier : 0.31306871841068806
Log loss (cv) on the stacking classifier : 0.5266414657786868
Log loss (train) on the stacking classifier : 0.5249250625775747
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
5.0 Final Observations Preetytableformat</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
| Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 |
| Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 |
| gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 |
| gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 |
| gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 |
| gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 |
| gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 |
| gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 |
| gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 |
| gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
- 3.1.2 Analysing Gene Feature </p> </div> </div> </div> Q1. what type of feature is gene ? </div> </div> </div> It is a Categorical Feature Q2.How many Categories are there? </div> </div> </div> df.groupby("Gene")["ID"].count() Gene ABL1 26 ACVR1 3 AGO2 5 AKT1 28 AKT2 11 .. WHSC1 1 WHSC1L1 1 XPO1 2 XRCC2 2 YAP1 4 Name: ID, Length: 264, dtype: int64 we can see that there are 263 unique Gene categories are present Q3. Distribution of Gene? </div> </div> </div> unique_genes = df['Gene'].value_counts() # the top 10 genes that occured most print(unique_genes.head(10)) BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 KIT 99 BRAF 93 ALK 69 ERBB2 69 PDGFRA 60 Name: Gene, dtype: int64 sums=sum(unique_genes.values) result=unique_genes.values/sums plt.plot(result,label="Histrogram of Genes") plt.xlabel('Index of a Gene') plt.ylabel('Number of Occurances') plt.legend() plt.grid() plt.show() There are some genes which occur very less , some genes which occur more plt.plot(np.cumsum(result),label='Cumulative distribution of Genes') plt.grid() plt.legend() plt.show() 80 Percent of the genes have index in range 50-75 Q4 How to featurize this Feature? Gene </div> </div> </div> 1.One-Hot Encoding 2.Mean-ResponseCoding 3.FeatureHashing source:-https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher </div> </div> </div> Lets Create data frames to work with this df_one_hot_encoding=df df_mean_response_coding=df df_featureHashing=df OneHotEncoding df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text']) df_one_hot_encoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene 0 FAM58A 1 CBL 2 CBL 3 CBL 4 CBL labelencode=LabelEncoder() df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene']) df_one_hot_encoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene Generated_lables 0 FAM58A 85 1 CBL 39 2 CBL 39 3 CBL 39 4 CBL 39 ... ... ... 3316 RUNX1 221 3317 RUNX1 221 3318 RUNX1 221 3319 RUNX1 221 3320 RUNX1 221 3321 rows × 2 columns list_feature_labels=list(labelencode.classes_) df_one_hot_encoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene Generated_lables 0 FAM58A 85 1 CBL 39 2 CBL 39 3 CBL 39 4 CBL 39 onehotencoder=OneHotEncoder() array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray() data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels) data_gen.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... SDHB SDHC SETD2 SF3B1 SHOC2 SHQ1 SMAD2 SMAD3 SMAD4 SMARCA4 SMARCB1 SMO SOS1 SOX9 SPOP SRC SRSF2 STAG2 STAT3 STK11 TCF3 TCF7L2 TERT TET1 TET2 TGFBR1 TGFBR2 TMPRSS2 TP53 TP53BP1 TSC1 TSC2 U2AF1 VEGFA VHL WHSC1 WHSC1L1 XPO1 XRCC2 YAP1 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 264 columns onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 ) onehotencoded_features_gene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables']) finalOneHotEncodedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene). finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv") Feature Hashing for GeneFeature We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes. df_featureHashing.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text']) df_featureHashing.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene 0 FAM58A 1 CBL 2 CBL 3 CBL 4 CBL hasher=FeatureHasher(n_features=9,input_type='string') hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray() dataframe_hashed_features=pd.DataFrame(hased_features) dataframe_hashed_features.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1) finalFeatureHashedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv") Now Lets go for meanResponseCoding df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... df_mean_response_coding.Gene.value_counts() BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 ... DUSP4 1 TCF7L2 1 VEGFA 1 ASXL1 1 ARID1A 1 Name: Gene, Length: 264, dtype: int64 df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text']) df_mean_response_coding.Gene.value_counts() BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 ... IKZF1 1 GNA11 1 INPP4B 1 FGF3 1 ARID1A 1 Name: Gene, Length: 264, dtype: int64 df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... len(df) 3321 dummy=df_mean_response_coding[0:3321] objects=dummy.Gene.value_counts() def myfunction_one(data): dummy1=data my_dicti={} for i,denominator in objects.items(): vector=[] for k in range (1,10): count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)]) vector.append( (count + (1*10)) / (denominator + (1*90)) ) my_dicti[i] = vector return my_dicti my_diciti=myfunction_one(dummy) def vectoresCreation(data): dummy1=data eachrow_vector_data=[ ] for index,row in dummy1.iterrows(): if row["Gene"] in dict(dummy1.Gene.value_counts()).keys(): eachrow_vector_data.append(my_diciti[row["Gene"]]) else : eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9]) return(eachrow_vector_data) eachRow_Vector=vectoresCreation(dummy) df_meanresponse_vectors=pd.DataFrame(eachRow_Vector) df_meanresponse_vectors.shape (3321, 9) df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1) df_meanresponse_vectors.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv") Q5.How good is this gene feature in predicting y_i? We can do this in many ways one is by simple plotting and other by simple models. Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well. finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0') finalFeatureHashedFeaturesOfGene.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 4 finalFeatureHashedFeaturesOfGene.shape (3321, 10) finalFeatureHashedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]] model to test def SVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) SVM(X_Hashed,y_true_Hashed) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) by seeing the above plot we take alpha as 0.001 def SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test)) SVMModel(X_Hashed,y_true_Hashed) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logg loss for training data with best aplha 0.001 is [1.7364535644919004] The Logg loss for cv data with best aplha 0.001 is [1.7737982623403665] The Logg loss for test data with best aplha 0.001 is [1.7470137046208585] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well lets go to meanresponse coding features and apply SVM finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0') finalMeanResponseVectorsOfGene.columns Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']] y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values def SVM_meanResponseCoding(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True) #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) We ca see that i can take c = 1 from above graph Lets go for testing on test data def SVM_meanResponseCoding_test(var1,var2): """ This function is use to build n test SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True) #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) predict_y3 =calibrated.predict_proba(x_test) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train)) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logg loss for training data with best aplha 1 is [1.1284981651581154] The Logg loss for cv data with best aplha 1 is [1.1436551747978385] The Logg loss for test data with best aplha 1 is [1.1082205650636348] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well. comming to Onehotencodded Features we can use logistic regression due to high dimentions finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0") finalOneHotFeatures.columns Index(['ABL1', 'ACVR1', 'AGO2', 'AKT1', 'AKT2', 'AKT3', 'ALK', 'APC', 'AR', 'ARAF', ... 'TSC2', 'U2AF1', 'VEGFA', 'VHL', 'WHSC1', 'WHSC1L1', 'XPO1', 'XRCC2', 'YAP1', 'Class'], dtype='object', length=265) x_oneHot=finalOneHotFeatures.iloc[:,0:264 ] y_true_oneHot=finalOneHotFeatures.Class.values Model def LogisticReg_tune(var1,var2): """ This Function is used to tune the logistic regression """ X=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train = [] logLoss_cv = [] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ] for i in c: clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) clf.fit(x_train , y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) LogisticReg_tune(x_oneHot,y_true_oneHot) (2124, 264) (2124,) (532, 264) (532,) (665, 264) (665,) by seeing the above graph i can take log loss as 0.1 . def LogisticReg_test(var1,var2): """ This Function is used to tune the logistic regression """ X=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train = [] logLoss_cv = [ ] logLoss_test=[ ] clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) clf.fit(x_train , y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) predict_y3 = calibrated.predict_proba(x_test) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train)) print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv )) print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test)) LogisticReg_test(x_oneHot,y_true_oneHot) (2124, 264) (2124,) (532, 264) (532,) (665, 264) (665,) The Logg loss for training data with best aplha 0.1 is [1.2638620900916107] The Logg loss for cv data with best aplha 0.1 is [1.3524898055298527] The Logg loss for test data with best aplha 0.1 is [1.3393455550993199] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well. Observations on Gene Featurisations table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470]) table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082]) table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Gene | FeatureHaser | SVM-kernel-RBF | 0.001 | 1.7364 | 1.7737 | 1.747 | | Gene | MeanResponseCoding | SVM-kernel-RBF | 1 | 1.1284 | 1.1436 | 1.1082 | | Gene | OneHotEncoding | LogisticRegression | 0.1 | 1.2638 | 1.3524 | 1.3393 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ SVM with less dimensions with meanResponseCoding worked well because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel. Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model. Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.3 Analysing of Variation Feature</p> </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... Q1. what is the Variation Feature Type? </div> </div> </div> It is Categorical </div> </div> </div> Q2.How many categories are present? </div> </div> </div> len(df.Variation.value_counts()) 2996 There are 2996 categories </div> </div> </div> Q3.What is the distribution of categories? </div> </div> </div> unique_variations=df.Variation.value_counts() sum0f_unique=sum(unique_variations) histoGram_variation=unique_variations.values / sum0f_unique plt.plot(histoGram_variation ,label = "Histogram of Variation") plt.xlabel(" index of Variation") plt.ylabel(" count") plt.legend() plt.grid() plt.show() by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0. cumsum_histogram=np.cumsum(histoGram_variation) plt.plot(cumsum_histogram,label='Cumulative distribution of variations') plt.grid() plt.legend() plt.show() Q4.How to Featurise this variation Feature? </div> </div> </div> There are few ways:- 1.OneHotEncoding 2.FeatureHasher 3.MeanResponseCoding </div> </div> </div> Lets Start with OneHotEncoding of VariationFeature ds_oneHotencoding=df ds_meanResponse_coding=df ds_featureHasher=df ds_oneHotencoding.columns Index(['ID', 'Gene', 'Variation', 'Class', 'Cleaned_text'], dtype='object') ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text']) ds_oneHotencoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation 0 Truncating Mutations 1 W802* 2 Q249E 3 N454D 4 L399V label_encoder=LabelEncoder() ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"]) ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns featurenames=label_encoder.classes_ ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns onehotencoder_Variation=OneHotEncoder() encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray() encoded_array array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]) ds_dash=pd.DataFrame(encoded_array , columns=featurenames) ds_dash.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insGLYVDFREYEY Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 2996 columns OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1) OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues']) OneHotEncoded_ds.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv") OneHotEncoded_ds .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3321 rows × 2997 columns Lets create feature hasher for variation features ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"]) ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Class 0 Truncating Mutations 1 1 W802* 2 2 Q249E 2 3 N454D 3 4 L399V 4 hasher= FeatureHasher(n_features=9,input_type='string') array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray() ds_dash_hashed=pd.DataFrame(array_hashed) hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1) hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv") Lets go for mean responsecoding of variation feature df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... objects_ThisFeature=df_mean_response_coding.Variation.value_counts() objects_ThisFeature Truncating Mutations 93 Deletion 74 Amplification 71 Fusions 34 Overexpression 6 .. K78A 1 A1789S 1 T401I 1 K550_V555delinsI 1 R420H 1 Name: Variation, Length: 2996, dtype: int64 my_dictionary_varFeature={ } for feature_name , feature_total_count in objects_ThisFeature.items(): vector_array_features=[ ] for index in range(1,10): count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)]) vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90))) my_dictionary_varFeature[feature_name] = vector_array_features my_dictionary_varFeature {'Truncating Mutations': [0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], 'Deletion': [0.40853658536585363, 0.06097560975609756, 0.06097560975609756, 0.15853658536585366, 0.06097560975609756, 0.06707317073170732, 0.06097560975609756, 0.06097560975609756, 0.06097560975609756], 'Amplification': [0.062111801242236024, 0.19254658385093168, 0.062111801242236024, 0.062111801242236024, 0.062111801242236024, 0.12422360248447205, 0.3105590062111801, 0.062111801242236024, 0.062111801242236024], 'Fusions': [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], 'Overexpression': [0.10416666666666667, 0.125, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.14583333333333334, 0.10416666666666667, 0.10416666666666667], 'G12V': [0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.14893617021276595, 0.10638297872340426, 0.10638297872340426], 'Q61H': [0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011], 'Q61L': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'E17K': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'T58I': [0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011], 'Q61R': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'P34R': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Q22K': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'S308A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Promoter Hypermethylation': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'F384L': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'E330K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G12C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Y42C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G67R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G13V': [0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'P130S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Q209L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'T286A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G12A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'I31M': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'A146V': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'E542K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'TMPRSS2-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'M1R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G35R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304], 'Y64A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R173C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'ETV6-NTRK3 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G13C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R170W': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G13D': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'K117N': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'C618R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'F28L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'Q61K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S222D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T73I': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R841K': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'V321M': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T167A': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'A146T': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'EWSR1-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S501_A502dup': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R287A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A57V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G309A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'W131G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V555_V559del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R683K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A18D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E239A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E439del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R776C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S241L': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'FGFR1-TACC1 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G42R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T160I': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'ROS1-CD74 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R373H': [0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N546K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R465H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V1676D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1686Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I122S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y599_D600insSTDNEYFYVDFREYEY': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1172L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R487W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L838P': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E127G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'L117P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T674I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P278A': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W24C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'X963_splice': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R2336H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A2770T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P81T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S464L': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D331G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A750_E758del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C482R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S36Y': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V465M': 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0.10989010989010989], 'L424V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V774A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H1094R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], '2010_2471trunc': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D816E': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'P551_V555del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T315I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K292T': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G1128S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T123A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], 'I744_K745delinsKIPVAI': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G81D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1841N': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D839G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1094Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 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0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C91S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V242F': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y553N': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D289_D292del': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K45T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M774_A775insAYVM': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'DNMT3B7': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989], 'S33Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G67S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G596C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H36P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'Y652H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'I1018W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V769_D770insGVV': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R282W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V11A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R420Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E82V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P186S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V155F': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G380R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C135R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S35Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T205A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746_A750del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D83V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K181M': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088], 'GIT2-PDGFRB Fusion': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T417I': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R80P': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R1097H': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I1170S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R283Q': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E1071W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P114L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F74S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R905Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W383R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R905W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N372H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'DNA binding domain deletions': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D473G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K550_W557del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T529N': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H61D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M1255I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N116H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L747_P753del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P480L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T1691K': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L1947R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E172K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V344A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L248V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G129E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D408E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F119S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H597Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'EGFRvIII': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E40K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A1752V': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G248V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989]] meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss) meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1) meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv") As we have all the vector forms of the featurisations lets proceed with further analysis Q5.How good is this Variation feature in predicting y_i? Lets Build simple models to check our selves As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library. 1.meanResponse coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> SVM Model meanResponseCoding_variationFeature.columns Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 'Class'], dtype='object') x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values x_true_meanresponse array([[0.54098361, 0.06010929, 0.05464481, ..., 0.05464481, 0.05464481, 0.05464481], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], ..., [0.09677419, 0.33064516, 0.08064516, ..., 0.08064516, 0.08870968, 0.08064516], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011]]) def svm_model_tuning(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) svm_model_tuning(x_true_meanresponse , y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) From above graph i select my best C as 1 Testing of the model def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) svm_model_testing(x_true_meanresponse,y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 1 the coresponding train loss is [0.13402600125222605] The Logloss for 1 the coresponding cv loss is [0.1479095316786708] The Logloss for 1 the coresponding test loss is [0.12267536305037746] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 2.Hashed coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0') hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values y_hashed_true=hashedEncodedFeatureof_variation['Class'].values We will reuse the same svm functions we defiend earilier svm_model_tuning(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) from the above graph i can choose my C as 0.0001 def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test )) svm_model_testing(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 0.0001 the coresponding train loss is [1.7335822874941234] The Logloss for 0.0001 the coresponding cv loss is [1.753463388538884] The Logloss for 0.0001 the coresponding test loss is [1.7599055604165221] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 3.OneHotencoding of variation feature As this has high dimention lets use logistic regression </div> </div> </div> oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0') oneHotEncodedfeaturesof_Variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns oneHotEncodedfeaturesof_Variation.columns Index(['1_2009trunc', '2010_2471trunc', '256_286trunc', '3' Deletion', '385_418del', '422_605trunc', '533_534del', '534_536del', '550_592del', '560_561insER', ... 'Y901C', 'Y931C', 'Y98H', 'Y98N', 'YAP1-FAM118B Fusion', 'YAP1-MAMLD1 Fusion', 'ZC3H7B-BCOR Fusion', 'ZNF198-FGFR1 Fusion', 'p61BRAF', 'Class'], dtype='object', length=2997) x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values LogisticModel def logistic_tune(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) logistic_tune(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) From the above graph i can choos aplha as 0.01 Testing model def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test )) logistic_test(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) The Logloss for 0.01 the coresponding train loss is [1.4238762684713966] The Logloss for 0.01 the coresponding cv loss is [1.744474382727282] The Logloss for 0.01 the coresponding test loss is [1.7380242209450858] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets compare all of them Lets compare all observations on Variation Feature table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599]) table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479]) table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Variation | FeatureHaser | SVM-kernel-RBF | 0.0001 | 1.7335 | 1.7534 | 1.7599 | | Variation | MeanResponseCoding | SVM-kernel-RBF | 1 | 0.134 | 0.1479 | 0.1479 | | Variation | OneHotEncoding | LogisticRegression | 0.01 | 1.4238 | 1.7444 | 1.738 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well. Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.4 Analysing of Text Feature</p> </div> </div> </div> Q1.What kind of feature is Text </div> </div> </div> It is sentance corpus consisting of many sentances. Q2.how can we featurise it There are many ways we will work with these two 1.BOW 2.TFIDF </div> </div> </div> 1.Featurising with bow df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... bow_df=df bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation']) array_input=bow_df.Cleaned_text.values bow_ds=df vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000) tf=vectoriser.fit(array_input) vocab_countVectoriser=(tf.vocabulary_) vocab_countVectoriser.items() dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 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'concept', 'conclude', 'concluded', 'conclusion', 'conclusions', 'concomitant', 'concordance', 'concurrent', 'condition', 'conditional', 'conditions', 'conducted', 'confer', 'confer resistance', 'conferred', 'conferring', 'confers', 'confidence', 'confirm', 'confirmation', 'confirmed', 'confirming', 'confluence', 'conformation', 'conformational', 'conformational change', 'conformational changes', 'conformations', 'congenital', 'conjugated', 'consecutive', 'consensus', 'consent', 'consequence', 'consequences', 'consequently', 'conservation', 'conservative', 'conserved', 'consider', 'considerable', 'considered', 'considering', 'consisted', 'consistent', 'consistent previous', 'consistently', 'consisting', 'consists', 'consortium', 'constant', 'constitute', 'constitutional', 'constitutive', 'constitutive activation', 'constitutively', 'constitutively activated', 'constitutively active', 'construct', 'constructed', 'construction', 'constructs', 'contact', 'contact help', 'contacts', 'contain', 'contained', 'containing', 'containing mm', 'contains', 'content', 'context', 'contexts', 'continued', 'continuous', 'contrast', 'contribute', 'contributed', 'contributes', 'contributing', 'contribution', 'control', 'control cells', 'controlled', 'controlling', 'controls', 'conventional', 'conversely', 'conversion', 'cooh', 'cooh terminal', 'cooperate', 'cooperative', 'copies', 'copy', 'copy number', 'core', 'core domain', 'core enzyme', 'correct', ...] array_transformed=vectoriser.transform(array_input) array_transformedone=array_transformed.toarray() dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow ) dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1) dataframofBowForText.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aa aacrjournals aacrjournals org ab abbreviations abc abd aberrant aberrant splicing aberrations abi ability ability bind ability induce abl able abnormal abnormalities abolish abolished abrogate abrogated absence absent absolute abstract abundance abundant ac acc accelerated accepted acceptor access access image accessible accessible alternative accession accompanied accordance ... withdrawal within without wm wm cells wnt women work worldwide worse wpd written wt wt mutant wt wt wtb wtb raf wu xenograft xenografts xenopus xl xp xrcc xu yap year years yeast yeast cells yellow yes yet yield yielded young zhang zinc zn Class 0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 ... 0 3 3 0 0 0 0 2 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 3 0 0 0 0 1 0 0 2 0 0 1 1 0 0 3 2 0 8 0 1 0 1 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ... 2 2 5 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 4 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 ... 0 4 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 10 4 5 rows × 5001 columns dataframofBowForText.to_csv("datafraneofBowTextFeature.csv") 2.TFIDF featurisation of text data </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... tfidf_df=df tfidf_ds=df tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class']) tfidf_df .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Cleaned_text 0 cyclin dependent kinases cdks regulate variety... 1 abstract background non small cell lung cancer... 2 abstract background non small cell lung cancer... 3 recent evidence demonstrated acquired uniparen... 4 oncogenic mutations monomeric casitas b lineag... ... ... 3316 introduction myelodysplastic syndromes mds het... 3317 introduction myelodysplastic syndromes mds het... 3318 runt related transcription factor gene runx al... 3319 runx gene frequent target chromosomal transloc... 3320 frequent mutations associated leukemia recurre... 3321 rows × 1 columns array_input_tfidf=tfidf_df['Cleaned_text'].values vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000) vectorizer.fit(array_input_tfidf) TfidfVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.float64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=1000, min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=True, stop_words=None, strip_accents=None, sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True, vocabulary=None) len(vectorizer.vocabulary_) 1000 featurenames_tfidf=vectorizer.get_feature_names() tfidf_vocab=vectorizer.vocabulary_ transformed_tfidf_csr=vectorizer.transform(array_input_tfidf) transformed_tfidf_csr=transformed_tfidf_csr.toarray() arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf) tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1) tfidf_textfeature.to_csv("tfidf_textfeatures.csv") tfidf_textfeature .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional adenocarcinoma advanced affect affected affecting affinity age akt al ala alk allele alleles alone alterations altered alternative although ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 0.000000 0.0 0.000000 0.007996 0.003394 0.003430 0.000000 0.000000 0.000000 0.0 0.0 0.005836 0.000000 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0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 0.000000 0.0 0.000000 0.026962 0.007630 0.000000 0.003861 0.000000 0.000000 0.0 0.0 0.009838 0.018076 0.003339 0.000000 0.000000 0.000000 0.017664 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 0.000000 0.0 0.010418 0.009430 0.007205 0.031548 0.000000 0.000000 0.000000 0.0 0.0 0.008259 0.000000 0.000000 0.000000 0.000000 0.004647 0.006950 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 0.000000 0.0 0.001547 0.000000 0.000000 0.000000 0.004330 0.000000 0.006224 0.0 0.0 0.000000 0.000000 0.013106 0.003385 0.000000 0.000000 0.004953 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 0.000000 0.0 0.001989 0.000000 0.000000 0.000000 0.005570 0.000000 0.008005 0.0 0.0 0.000000 0.000000 0.016859 0.002177 0.000000 0.000000 0.006371 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.003138 0.000000 0.000000 0.005647 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.001402 0.001502 0.002697 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 0.000000 0.0 0.000000 0.018975 0.000000 0.002713 0.005434 0.000000 0.093724 0.0 0.0 0.013848 0.005089 0.011748 0.025491 0.000000 0.023376 0.004662 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 0.000000 0.0 0.003222 0.025663 0.000000 0.009006 0.003006 0.000000 0.055096 0.0 0.0 0.010216 0.004223 0.010400 0.015279 0.000000 0.014371 0.005158 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1001 columns Q3. Is this Bow,tfidf for text feature Useful in our classification? </div> </div> </div> Lets find out by tsne and one simple model. TSNE for BOW TEXT Feature x_std=dataframofBowForText.iloc[:, 0:5000].values y_true_std=dataframofBowForText.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_std) intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df10,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Tsne for TFIDF data x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values y_tfidf_true=tfidf_textfeature.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_tfidf_true) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df12,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Lets do analisys based on models for the two featurisations and compare the results Lets go with simple model like logistic regression for high dimension data 1.BOW Feature for text def oneHot_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) oneHot_Logsitic( data,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data oneHot_Logsitic( x_std,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one is without standardised data from the graphs i can take my alpha as 0.001 Testing on test datasets def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test )) logistic_test(x_std,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0724656346673704] The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368] The Logloss for 0.001 the coresponding test loss is [1.3034431218014317] Above is result on normal data logistic_test(data,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9441582057917238] The Logloss for 0.001 the coresponding cv loss is [1.210222073584811] The Logloss for 0.001 the coresponding test loss is [1.1539667998430334] Above is Result on standardised data we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well. 2.TFIDF feauture for text Using Logisticmodels we bult for previous cases Tuning oneHot_Logsitic(x_tfidf_true,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was without standardised data oneHot_Logsitic(data,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data From the graphs i can select my alpha as 0.001 Lets Test the model without standardised data logistic_test(x_tfidf_true,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0655639055266641] The Logloss for 0.001 the coresponding cv loss is [1.287554414069922] The Logloss for 0.001 the coresponding test loss is [1.2673057224719144] with standardised data logistic_test(data,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9062325983187661] The Logloss for 0.001 the coresponding cv loss is [1.154476481391309] The Logloss for 0.001 the coresponding test loss is [1.2146295572433117] Observations of Text featureised models with Standardised data only</p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 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-0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> Q1. what type of feature is gene ? </div> </div> </div> It is a Categorical Feature Q2.How many Categories are there? </div> </div> </div> df.groupby("Gene")["ID"].count() Gene ABL1 26 ACVR1 3 AGO2 5 AKT1 28 AKT2 11 .. WHSC1 1 WHSC1L1 1 XPO1 2 XRCC2 2 YAP1 4 Name: ID, Length: 264, dtype: int64 we can see that there are 263 unique Gene categories are present Q3. Distribution of Gene? </div> </div> </div> unique_genes = df['Gene'].value_counts() # the top 10 genes that occured most print(unique_genes.head(10)) BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 KIT 99 BRAF 93 ALK 69 ERBB2 69 PDGFRA 60 Name: Gene, dtype: int64 sums=sum(unique_genes.values) result=unique_genes.values/sums plt.plot(result,label="Histrogram of Genes") plt.xlabel('Index of a Gene') plt.ylabel('Number of Occurances') plt.legend() plt.grid() plt.show() There are some genes which occur very less , some genes which occur more plt.plot(np.cumsum(result),label='Cumulative distribution of Genes') plt.grid() plt.legend() plt.show() 80 Percent of the genes have index in range 50-75 Q4 How to featurize this Feature? Gene </div> </div> </div> 1.One-Hot Encoding 2.Mean-ResponseCoding 3.FeatureHashing source:-https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher </div> </div> </div> Lets Create data frames to work with this df_one_hot_encoding=df df_mean_response_coding=df df_featureHashing=df OneHotEncoding df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text']) df_one_hot_encoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene 0 FAM58A 1 CBL 2 CBL 3 CBL 4 CBL labelencode=LabelEncoder() df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene']) df_one_hot_encoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene Generated_lables 0 FAM58A 85 1 CBL 39 2 CBL 39 3 CBL 39 4 CBL 39 ... ... ... 3316 RUNX1 221 3317 RUNX1 221 3318 RUNX1 221 3319 RUNX1 221 3320 RUNX1 221 3321 rows × 2 columns list_feature_labels=list(labelencode.classes_) df_one_hot_encoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene Generated_lables 0 FAM58A 85 1 CBL 39 2 CBL 39 3 CBL 39 4 CBL 39 onehotencoder=OneHotEncoder() array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray() data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels) data_gen.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... SDHB SDHC SETD2 SF3B1 SHOC2 SHQ1 SMAD2 SMAD3 SMAD4 SMARCA4 SMARCB1 SMO SOS1 SOX9 SPOP SRC SRSF2 STAG2 STAT3 STK11 TCF3 TCF7L2 TERT TET1 TET2 TGFBR1 TGFBR2 TMPRSS2 TP53 TP53BP1 TSC1 TSC2 U2AF1 VEGFA VHL WHSC1 WHSC1L1 XPO1 XRCC2 YAP1 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 264 columns onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 ) onehotencoded_features_gene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables']) finalOneHotEncodedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene). finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv") Feature Hashing for GeneFeature We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes. df_featureHashing.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text']) df_featureHashing.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Gene 0 FAM58A 1 CBL 2 CBL 3 CBL 4 CBL hasher=FeatureHasher(n_features=9,input_type='string') hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray() dataframe_hashed_features=pd.DataFrame(hased_features) dataframe_hashed_features.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1) finalFeatureHashedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv") Now Lets go for meanResponseCoding df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... df_mean_response_coding.Gene.value_counts() BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 ... DUSP4 1 TCF7L2 1 VEGFA 1 ASXL1 1 ARID1A 1 Name: Gene, Length: 264, dtype: int64 df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text']) df_mean_response_coding.Gene.value_counts() BRCA1 264 TP53 163 EGFR 141 PTEN 126 BRCA2 125 ... IKZF1 1 GNA11 1 INPP4B 1 FGF3 1 ARID1A 1 Name: Gene, Length: 264, dtype: int64 df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... len(df) 3321 dummy=df_mean_response_coding[0:3321] objects=dummy.Gene.value_counts() def myfunction_one(data): dummy1=data my_dicti={} for i,denominator in objects.items(): vector=[] for k in range (1,10): count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)]) vector.append( (count + (1*10)) / (denominator + (1*90)) ) my_dicti[i] = vector return my_dicti my_diciti=myfunction_one(dummy) def vectoresCreation(data): dummy1=data eachrow_vector_data=[ ] for index,row in dummy1.iterrows(): if row["Gene"] in dict(dummy1.Gene.value_counts()).keys(): eachrow_vector_data.append(my_diciti[row["Gene"]]) else : eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9]) return(eachrow_vector_data) eachRow_Vector=vectoresCreation(dummy) df_meanresponse_vectors=pd.DataFrame(eachRow_Vector) df_meanresponse_vectors.shape (3321, 9) df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1) df_meanresponse_vectors.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv") Q5.How good is this gene feature in predicting y_i? We can do this in many ways one is by simple plotting and other by simple models. Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well. finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0') finalFeatureHashedFeaturesOfGene.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 4 finalFeatureHashedFeaturesOfGene.shape (3321, 10) finalFeatureHashedFeaturesOfGene.Class.value_counts() 7 953 4 686 1 568 2 452 6 275 5 242 3 89 9 37 8 19 Name: Class, dtype: int64 y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]] model to test def SVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) SVM(X_Hashed,y_true_Hashed) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) by seeing the above plot we take alpha as 0.001 def SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test)) SVMModel(X_Hashed,y_true_Hashed) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logg loss for training data with best aplha 0.001 is [1.7364535644919004] The Logg loss for cv data with best aplha 0.001 is [1.7737982623403665] The Logg loss for test data with best aplha 0.001 is [1.7470137046208585] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well lets go to meanresponse coding features and apply SVM finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0') finalMeanResponseVectorsOfGene.columns Index(['0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']] y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values def SVM_meanResponseCoding(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True) #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) We ca see that i can take c = 1 from above graph Lets go for testing on test data def SVM_meanResponseCoding_test(var1,var2): """ This function is use to build n test SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True) #clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) predict_y3 =calibrated.predict_proba(x_test) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train)) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse) (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logg loss for training data with best aplha 1 is [1.1284981651581154] The Logg loss for cv data with best aplha 1 is [1.1436551747978385] The Logg loss for test data with best aplha 1 is [1.1082205650636348] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well. comming to Onehotencodded Features we can use logistic regression due to high dimentions finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0") finalOneHotFeatures.columns Index(['ABL1', 'ACVR1', 'AGO2', 'AKT1', 'AKT2', 'AKT3', 'ALK', 'APC', 'AR', 'ARAF', ... 'TSC2', 'U2AF1', 'VEGFA', 'VHL', 'WHSC1', 'WHSC1L1', 'XPO1', 'XRCC2', 'YAP1', 'Class'], dtype='object', length=265) x_oneHot=finalOneHotFeatures.iloc[:,0:264 ] y_true_oneHot=finalOneHotFeatures.Class.values Model def LogisticReg_tune(var1,var2): """ This Function is used to tune the logistic regression """ X=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train = [] logLoss_cv = [] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ] for i in c: clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) clf.fit(x_train , y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) LogisticReg_tune(x_oneHot,y_true_oneHot) (2124, 264) (2124,) (532, 264) (532,) (665, 264) (665,) by seeing the above graph i can take log loss as 0.1 . def LogisticReg_test(var1,var2): """ This Function is used to tune the logistic regression """ X=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_train = [] logLoss_cv = [ ] logLoss_test=[ ] clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) clf.fit(x_train , y_train) calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) predict_y3 = calibrated.predict_proba(x_test) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train)) print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv )) print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test)) LogisticReg_test(x_oneHot,y_true_oneHot) (2124, 264) (2124,) (532, 264) (532,) (665, 264) (665,) The Logg loss for training data with best aplha 0.1 is [1.2638620900916107] The Logg loss for cv data with best aplha 0.1 is [1.3524898055298527] The Logg loss for test data with best aplha 0.1 is [1.3393455550993199] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well. Observations on Gene Featurisations table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470]) table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082]) table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Gene | FeatureHaser | SVM-kernel-RBF | 0.001 | 1.7364 | 1.7737 | 1.747 | | Gene | MeanResponseCoding | SVM-kernel-RBF | 1 | 1.1284 | 1.1436 | 1.1082 | | Gene | OneHotEncoding | LogisticRegression | 0.1 | 1.2638 | 1.3524 | 1.3393 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ SVM with less dimensions with meanResponseCoding worked well because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel. Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model. Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.3 Analysing of Variation Feature</p> </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... Q1. what is the Variation Feature Type? </div> </div> </div> It is Categorical </div> </div> </div> Q2.How many categories are present? </div> </div> </div> len(df.Variation.value_counts()) 2996 There are 2996 categories </div> </div> </div> Q3.What is the distribution of categories? </div> </div> </div> unique_variations=df.Variation.value_counts() sum0f_unique=sum(unique_variations) histoGram_variation=unique_variations.values / sum0f_unique plt.plot(histoGram_variation ,label = "Histogram of Variation") plt.xlabel(" index of Variation") plt.ylabel(" count") plt.legend() plt.grid() plt.show() by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0. cumsum_histogram=np.cumsum(histoGram_variation) plt.plot(cumsum_histogram,label='Cumulative distribution of variations') plt.grid() plt.legend() plt.show() Q4.How to Featurise this variation Feature? </div> </div> </div> There are few ways:- 1.OneHotEncoding 2.FeatureHasher 3.MeanResponseCoding </div> </div> </div> Lets Start with OneHotEncoding of VariationFeature ds_oneHotencoding=df ds_meanResponse_coding=df ds_featureHasher=df ds_oneHotencoding.columns Index(['ID', 'Gene', 'Variation', 'Class', 'Cleaned_text'], dtype='object') ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text']) ds_oneHotencoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation 0 Truncating Mutations 1 W802* 2 Q249E 3 N454D 4 L399V label_encoder=LabelEncoder() ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"]) ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns featurenames=label_encoder.classes_ ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns onehotencoder_Variation=OneHotEncoder() encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray() encoded_array array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]) ds_dash=pd.DataFrame(encoded_array , columns=featurenames) ds_dash.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insGLYVDFREYEY Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 2996 columns OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1) OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues']) OneHotEncoded_ds.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv") OneHotEncoded_ds .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3321 rows × 2997 columns Lets create feature hasher for variation features ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"]) ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Class 0 Truncating Mutations 1 1 W802* 2 2 Q249E 2 3 N454D 3 4 L399V 4 hasher= FeatureHasher(n_features=9,input_type='string') array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray() ds_dash_hashed=pd.DataFrame(array_hashed) hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1) hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv") Lets go for mean responsecoding of variation feature df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... objects_ThisFeature=df_mean_response_coding.Variation.value_counts() objects_ThisFeature Truncating Mutations 93 Deletion 74 Amplification 71 Fusions 34 Overexpression 6 .. K78A 1 A1789S 1 T401I 1 K550_V555delinsI 1 R420H 1 Name: Variation, Length: 2996, dtype: int64 my_dictionary_varFeature={ } for feature_name , feature_total_count in objects_ThisFeature.items(): vector_array_features=[ ] for index in range(1,10): count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)]) vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90))) my_dictionary_varFeature[feature_name] = vector_array_features my_dictionary_varFeature {'Truncating Mutations': [0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], 'Deletion': [0.40853658536585363, 0.06097560975609756, 0.06097560975609756, 0.15853658536585366, 0.06097560975609756, 0.06707317073170732, 0.06097560975609756, 0.06097560975609756, 0.06097560975609756], 'Amplification': [0.062111801242236024, 0.19254658385093168, 0.062111801242236024, 0.062111801242236024, 0.062111801242236024, 0.12422360248447205, 0.3105590062111801, 0.062111801242236024, 0.062111801242236024], 'Fusions': [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], 'Overexpression': [0.10416666666666667, 0.125, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.14583333333333334, 0.10416666666666667, 0.10416666666666667], 'G12V': [0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.14893617021276595, 0.10638297872340426, 0.10638297872340426], 'Q61H': [0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011], 'Q61L': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'E17K': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'T58I': [0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011], 'Q61R': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'P34R': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Q22K': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'S308A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Promoter Hypermethylation': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'F384L': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'E330K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G12C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Y42C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G67R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G13V': [0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'P130S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Q209L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'T286A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G12A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'I31M': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'A146V': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'E542K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'TMPRSS2-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'M1R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G35R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304], 'Y64A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R173C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'ETV6-NTRK3 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G13C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R170W': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G13D': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'K117N': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'C618R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'F28L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'Q61K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S222D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T73I': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R841K': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'V321M': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T167A': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'A146T': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'EWSR1-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S501_A502dup': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R287A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A57V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G309A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'W131G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V555_V559del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R683K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A18D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E239A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E439del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R776C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S241L': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N546K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R465H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V1676D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1686Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I122S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y599_D600insSTDNEYFYVDFREYEY': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1172L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R487W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L838P': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E127G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'L117P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T674I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P278A': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W24C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'X963_splice': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R2336H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A2770T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P81T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S464L': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D331G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A750_E758del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C482R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S36Y': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V465M': 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0.10989010989010989, 0.10989010989010989], 'D1733G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], '963_D1010splice': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'DNA binding domain insertions': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L861R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E1682K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 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0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'P551_V555del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T315I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K292T': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G1128S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T123A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], 'I744_K745delinsKIPVAI': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G81D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1841N': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D839G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1094Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 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0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C91S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V242F': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y553N': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D289_D292del': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K45T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M774_A775insAYVM': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'DNMT3B7': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989], 'S33Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G67S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G596C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H36P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'Y652H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'I1018W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V769_D770insGVV': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R282W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V11A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R420Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E82V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P186S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V155F': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G380R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C135R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S35Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T205A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746_A750del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D83V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K181M': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088], 'GIT2-PDGFRB Fusion': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T417I': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R80P': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R1097H': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I1170S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R283Q': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E1071W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P114L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F74S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R905Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W383R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R905W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N372H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'DNA binding domain deletions': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D473G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K550_W557del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T529N': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H61D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M1255I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N116H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L747_P753del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P480L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T1691K': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L1947R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E172K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V344A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L248V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G129E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D408E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F119S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H597Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'EGFRvIII': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E40K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A1752V': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G248V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989]] meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss) meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1) meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv") As we have all the vector forms of the featurisations lets proceed with further analysis Q5.How good is this Variation feature in predicting y_i? Lets Build simple models to check our selves As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library. 1.meanResponse coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> SVM Model meanResponseCoding_variationFeature.columns Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 'Class'], dtype='object') x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values x_true_meanresponse array([[0.54098361, 0.06010929, 0.05464481, ..., 0.05464481, 0.05464481, 0.05464481], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], ..., [0.09677419, 0.33064516, 0.08064516, ..., 0.08064516, 0.08870968, 0.08064516], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011]]) def svm_model_tuning(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) svm_model_tuning(x_true_meanresponse , y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) From above graph i select my best C as 1 Testing of the model def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) svm_model_testing(x_true_meanresponse,y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 1 the coresponding train loss is [0.13402600125222605] The Logloss for 1 the coresponding cv loss is [0.1479095316786708] The Logloss for 1 the coresponding test loss is [0.12267536305037746] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 2.Hashed coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0') hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values y_hashed_true=hashedEncodedFeatureof_variation['Class'].values We will reuse the same svm functions we defiend earilier svm_model_tuning(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) from the above graph i can choose my C as 0.0001 def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test )) svm_model_testing(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 0.0001 the coresponding train loss is [1.7335822874941234] The Logloss for 0.0001 the coresponding cv loss is [1.753463388538884] The Logloss for 0.0001 the coresponding test loss is [1.7599055604165221] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 3.OneHotencoding of variation feature As this has high dimention lets use logistic regression </div> </div> </div> oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0') oneHotEncodedfeaturesof_Variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns oneHotEncodedfeaturesof_Variation.columns Index(['1_2009trunc', '2010_2471trunc', '256_286trunc', '3' Deletion', '385_418del', '422_605trunc', '533_534del', '534_536del', '550_592del', '560_561insER', ... 'Y901C', 'Y931C', 'Y98H', 'Y98N', 'YAP1-FAM118B Fusion', 'YAP1-MAMLD1 Fusion', 'ZC3H7B-BCOR Fusion', 'ZNF198-FGFR1 Fusion', 'p61BRAF', 'Class'], dtype='object', length=2997) x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values LogisticModel def logistic_tune(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) logistic_tune(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) From the above graph i can choos aplha as 0.01 Testing model def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test )) logistic_test(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) The Logloss for 0.01 the coresponding train loss is [1.4238762684713966] The Logloss for 0.01 the coresponding cv loss is [1.744474382727282] The Logloss for 0.01 the coresponding test loss is [1.7380242209450858] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets compare all of them Lets compare all observations on Variation Feature table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599]) table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479]) table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Variation | FeatureHaser | SVM-kernel-RBF | 0.0001 | 1.7335 | 1.7534 | 1.7599 | | Variation | MeanResponseCoding | SVM-kernel-RBF | 1 | 0.134 | 0.1479 | 0.1479 | | Variation | OneHotEncoding | LogisticRegression | 0.01 | 1.4238 | 1.7444 | 1.738 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well. Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.4 Analysing of Text Feature</p> </div> </div> </div> Q1.What kind of feature is Text </div> </div> </div> It is sentance corpus consisting of many sentances. Q2.how can we featurise it There are many ways we will work with these two 1.BOW 2.TFIDF </div> </div> </div> 1.Featurising with bow df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... bow_df=df bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation']) array_input=bow_df.Cleaned_text.values bow_ds=df vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000) tf=vectoriser.fit(array_input) vocab_countVectoriser=(tf.vocabulary_) vocab_countVectoriser.items() dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 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'concept', 'conclude', 'concluded', 'conclusion', 'conclusions', 'concomitant', 'concordance', 'concurrent', 'condition', 'conditional', 'conditions', 'conducted', 'confer', 'confer resistance', 'conferred', 'conferring', 'confers', 'confidence', 'confirm', 'confirmation', 'confirmed', 'confirming', 'confluence', 'conformation', 'conformational', 'conformational change', 'conformational changes', 'conformations', 'congenital', 'conjugated', 'consecutive', 'consensus', 'consent', 'consequence', 'consequences', 'consequently', 'conservation', 'conservative', 'conserved', 'consider', 'considerable', 'considered', 'considering', 'consisted', 'consistent', 'consistent previous', 'consistently', 'consisting', 'consists', 'consortium', 'constant', 'constitute', 'constitutional', 'constitutive', 'constitutive activation', 'constitutively', 'constitutively activated', 'constitutively active', 'construct', 'constructed', 'construction', 'constructs', 'contact', 'contact help', 'contacts', 'contain', 'contained', 'containing', 'containing mm', 'contains', 'content', 'context', 'contexts', 'continued', 'continuous', 'contrast', 'contribute', 'contributed', 'contributes', 'contributing', 'contribution', 'control', 'control cells', 'controlled', 'controlling', 'controls', 'conventional', 'conversely', 'conversion', 'cooh', 'cooh terminal', 'cooperate', 'cooperative', 'copies', 'copy', 'copy number', 'core', 'core domain', 'core enzyme', 'correct', ...] array_transformed=vectoriser.transform(array_input) array_transformedone=array_transformed.toarray() dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow ) dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1) dataframofBowForText.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aa aacrjournals aacrjournals org ab abbreviations abc abd aberrant aberrant splicing aberrations abi ability ability bind ability induce abl able abnormal abnormalities abolish abolished abrogate abrogated absence absent absolute abstract abundance abundant ac acc accelerated accepted acceptor access access image accessible accessible alternative accession accompanied accordance ... withdrawal within without wm wm cells wnt women work worldwide worse wpd written wt wt mutant wt wt wtb wtb raf wu xenograft xenografts xenopus xl xp xrcc xu yap year years yeast yeast cells yellow yes yet yield yielded young zhang zinc zn Class 0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 ... 0 3 3 0 0 0 0 2 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 3 0 0 0 0 1 0 0 2 0 0 1 1 0 0 3 2 0 8 0 1 0 1 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ... 2 2 5 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 4 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 ... 0 4 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 10 4 5 rows × 5001 columns dataframofBowForText.to_csv("datafraneofBowTextFeature.csv") 2.TFIDF featurisation of text data </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... tfidf_df=df tfidf_ds=df tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class']) tfidf_df .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Cleaned_text 0 cyclin dependent kinases cdks regulate variety... 1 abstract background non small cell lung cancer... 2 abstract background non small cell lung cancer... 3 recent evidence demonstrated acquired uniparen... 4 oncogenic mutations monomeric casitas b lineag... ... ... 3316 introduction myelodysplastic syndromes mds het... 3317 introduction myelodysplastic syndromes mds het... 3318 runt related transcription factor gene runx al... 3319 runx gene frequent target chromosomal transloc... 3320 frequent mutations associated leukemia recurre... 3321 rows × 1 columns array_input_tfidf=tfidf_df['Cleaned_text'].values vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000) vectorizer.fit(array_input_tfidf) TfidfVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.float64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=1000, min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=True, stop_words=None, strip_accents=None, sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True, vocabulary=None) len(vectorizer.vocabulary_) 1000 featurenames_tfidf=vectorizer.get_feature_names() tfidf_vocab=vectorizer.vocabulary_ transformed_tfidf_csr=vectorizer.transform(array_input_tfidf) transformed_tfidf_csr=transformed_tfidf_csr.toarray() arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf) tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1) tfidf_textfeature.to_csv("tfidf_textfeatures.csv") tfidf_textfeature .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional adenocarcinoma advanced affect affected affecting affinity age akt al ala alk allele alleles alone alterations altered alternative although ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 0.000000 0.0 0.000000 0.007996 0.003394 0.003430 0.000000 0.000000 0.000000 0.0 0.0 0.005836 0.000000 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0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 0.000000 0.0 0.000000 0.026962 0.007630 0.000000 0.003861 0.000000 0.000000 0.0 0.0 0.009838 0.018076 0.003339 0.000000 0.000000 0.000000 0.017664 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 0.000000 0.0 0.010418 0.009430 0.007205 0.031548 0.000000 0.000000 0.000000 0.0 0.0 0.008259 0.000000 0.000000 0.000000 0.000000 0.004647 0.006950 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 0.000000 0.0 0.001547 0.000000 0.000000 0.000000 0.004330 0.000000 0.006224 0.0 0.0 0.000000 0.000000 0.013106 0.003385 0.000000 0.000000 0.004953 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 0.000000 0.0 0.001989 0.000000 0.000000 0.000000 0.005570 0.000000 0.008005 0.0 0.0 0.000000 0.000000 0.016859 0.002177 0.000000 0.000000 0.006371 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.003138 0.000000 0.000000 0.005647 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.001402 0.001502 0.002697 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 0.000000 0.0 0.000000 0.018975 0.000000 0.002713 0.005434 0.000000 0.093724 0.0 0.0 0.013848 0.005089 0.011748 0.025491 0.000000 0.023376 0.004662 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 0.000000 0.0 0.003222 0.025663 0.000000 0.009006 0.003006 0.000000 0.055096 0.0 0.0 0.010216 0.004223 0.010400 0.015279 0.000000 0.014371 0.005158 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1001 columns Q3. Is this Bow,tfidf for text feature Useful in our classification? </div> </div> </div> Lets find out by tsne and one simple model. TSNE for BOW TEXT Feature x_std=dataframofBowForText.iloc[:, 0:5000].values y_true_std=dataframofBowForText.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_std) intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df10,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Tsne for TFIDF data x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values y_tfidf_true=tfidf_textfeature.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_tfidf_true) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df12,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Lets do analisys based on models for the two featurisations and compare the results Lets go with simple model like logistic regression for high dimension data 1.BOW Feature for text def oneHot_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) oneHot_Logsitic( data,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data oneHot_Logsitic( x_std,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one is without standardised data from the graphs i can take my alpha as 0.001 Testing on test datasets def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test )) logistic_test(x_std,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0724656346673704] The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368] The Logloss for 0.001 the coresponding test loss is [1.3034431218014317] Above is result on normal data logistic_test(data,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9441582057917238] The Logloss for 0.001 the coresponding cv loss is [1.210222073584811] The Logloss for 0.001 the coresponding test loss is [1.1539667998430334] Above is Result on standardised data we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well. 2.TFIDF feauture for text Using Logisticmodels we bult for previous cases Tuning oneHot_Logsitic(x_tfidf_true,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was without standardised data oneHot_Logsitic(data,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data From the graphs i can select my alpha as 0.001 Lets Test the model without standardised data logistic_test(x_tfidf_true,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0655639055266641] The Logloss for 0.001 the coresponding cv loss is [1.287554414069922] The Logloss for 0.001 the coresponding test loss is [1.2673057224719144] with standardised data logistic_test(data,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9062325983187661] The Logloss for 0.001 the coresponding cv loss is [1.154476481391309] The Logloss for 0.001 the coresponding test loss is [1.2146295572433117] Observations of Text featureised models with Standardised data only</p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 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-0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- 3.1.3 Analysing of Variation Feature</p> </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... Q1. what is the Variation Feature Type? </div> </div> </div> It is Categorical </div> </div> </div> Q2.How many categories are present? </div> </div> </div> len(df.Variation.value_counts()) 2996 There are 2996 categories </div> </div> </div> Q3.What is the distribution of categories? </div> </div> </div> unique_variations=df.Variation.value_counts() sum0f_unique=sum(unique_variations) histoGram_variation=unique_variations.values / sum0f_unique plt.plot(histoGram_variation ,label = "Histogram of Variation") plt.xlabel(" index of Variation") plt.ylabel(" count") plt.legend() plt.grid() plt.show() by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0. cumsum_histogram=np.cumsum(histoGram_variation) plt.plot(cumsum_histogram,label='Cumulative distribution of variations') plt.grid() plt.legend() plt.show() Q4.How to Featurise this variation Feature? </div> </div> </div> There are few ways:- 1.OneHotEncoding 2.FeatureHasher 3.MeanResponseCoding </div> </div> </div> Lets Start with OneHotEncoding of VariationFeature ds_oneHotencoding=df ds_meanResponse_coding=df ds_featureHasher=df ds_oneHotencoding.columns Index(['ID', 'Gene', 'Variation', 'Class', 'Cleaned_text'], dtype='object') ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text']) ds_oneHotencoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation 0 Truncating Mutations 1 W802* 2 Q249E 3 N454D 4 L399V label_encoder=LabelEncoder() ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"]) ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns featurenames=label_encoder.classes_ ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns onehotencoder_Variation=OneHotEncoder() encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray() encoded_array array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]) ds_dash=pd.DataFrame(encoded_array , columns=featurenames) ds_dash.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insGLYVDFREYEY Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 2996 columns OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1) OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues']) OneHotEncoded_ds.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv") OneHotEncoded_ds .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3321 rows × 2997 columns Lets create feature hasher for variation features ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"]) ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Class 0 Truncating Mutations 1 1 W802* 2 2 Q249E 2 3 N454D 3 4 L399V 4 hasher= FeatureHasher(n_features=9,input_type='string') array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray() ds_dash_hashed=pd.DataFrame(array_hashed) hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1) hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv") Lets go for mean responsecoding of variation feature df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... objects_ThisFeature=df_mean_response_coding.Variation.value_counts() objects_ThisFeature Truncating Mutations 93 Deletion 74 Amplification 71 Fusions 34 Overexpression 6 .. K78A 1 A1789S 1 T401I 1 K550_V555delinsI 1 R420H 1 Name: Variation, Length: 2996, dtype: int64 my_dictionary_varFeature={ } for feature_name , feature_total_count in objects_ThisFeature.items(): vector_array_features=[ ] for index in range(1,10): count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)]) vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90))) my_dictionary_varFeature[feature_name] = vector_array_features my_dictionary_varFeature {'Truncating Mutations': [0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], 'Deletion': [0.40853658536585363, 0.06097560975609756, 0.06097560975609756, 0.15853658536585366, 0.06097560975609756, 0.06707317073170732, 0.06097560975609756, 0.06097560975609756, 0.06097560975609756], 'Amplification': [0.062111801242236024, 0.19254658385093168, 0.062111801242236024, 0.062111801242236024, 0.062111801242236024, 0.12422360248447205, 0.3105590062111801, 0.062111801242236024, 0.062111801242236024], 'Fusions': [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], 'Overexpression': [0.10416666666666667, 0.125, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.14583333333333334, 0.10416666666666667, 0.10416666666666667], 'G12V': [0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.14893617021276595, 0.10638297872340426, 0.10638297872340426], 'Q61H': [0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011], 'Q61L': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'E17K': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'T58I': [0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011], 'Q61R': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'P34R': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Q22K': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'S308A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Promoter Hypermethylation': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'F384L': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'E330K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G12C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Y42C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G67R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G13V': [0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'P130S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Q209L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'T286A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G12A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'I31M': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'A146V': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'E542K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'TMPRSS2-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'M1R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G35R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304], 'Y64A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R173C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'ETV6-NTRK3 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G13C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R170W': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G13D': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'K117N': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'C618R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'F28L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'Q61K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S222D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T73I': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R841K': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'V321M': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T167A': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'A146T': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'EWSR1-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S501_A502dup': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R287A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A57V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G309A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'W131G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V555_V559del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R683K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A18D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E239A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E439del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R776C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S241L': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'FGFR1-TACC1 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G42R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T160I': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'ROS1-CD74 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R373H': [0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N546K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R465H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V1676D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1686Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I122S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y599_D600insSTDNEYFYVDFREYEY': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1172L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R487W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L838P': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E127G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'L117P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T674I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P278A': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W24C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'X963_splice': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R2336H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A2770T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P81T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S464L': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D331G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A750_E758del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C482R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S36Y': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V465M': 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0.10989010989010989], 'L424V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V774A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H1094R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], '2010_2471trunc': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D816E': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'P551_V555del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T315I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K292T': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G1128S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T123A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], 'I744_K745delinsKIPVAI': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G81D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1841N': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D839G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1094Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 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0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C91S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V242F': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y553N': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D289_D292del': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K45T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M774_A775insAYVM': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'DNMT3B7': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989], 'S33Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G67S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G596C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H36P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'Y652H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'I1018W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V769_D770insGVV': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R282W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V11A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R420Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E82V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P186S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V155F': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G380R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C135R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S35Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T205A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746_A750del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D83V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K181M': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088], 'GIT2-PDGFRB Fusion': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T417I': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R80P': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R1097H': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I1170S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R283Q': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E1071W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P114L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F74S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R905Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W383R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R905W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N372H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'DNA binding domain deletions': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D473G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K550_W557del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T529N': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H61D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M1255I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N116H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L747_P753del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P480L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T1691K': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L1947R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E172K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V344A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L248V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G129E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D408E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F119S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H597Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'EGFRvIII': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E40K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A1752V': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G248V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989]] meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss) meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1) meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv") As we have all the vector forms of the featurisations lets proceed with further analysis Q5.How good is this Variation feature in predicting y_i? Lets Build simple models to check our selves As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library. 1.meanResponse coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> SVM Model meanResponseCoding_variationFeature.columns Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 'Class'], dtype='object') x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values x_true_meanresponse array([[0.54098361, 0.06010929, 0.05464481, ..., 0.05464481, 0.05464481, 0.05464481], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], ..., [0.09677419, 0.33064516, 0.08064516, ..., 0.08064516, 0.08870968, 0.08064516], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011]]) def svm_model_tuning(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) svm_model_tuning(x_true_meanresponse , y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) From above graph i select my best C as 1 Testing of the model def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) svm_model_testing(x_true_meanresponse,y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 1 the coresponding train loss is [0.13402600125222605] The Logloss for 1 the coresponding cv loss is [0.1479095316786708] The Logloss for 1 the coresponding test loss is [0.12267536305037746] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 2.Hashed coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0') hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values y_hashed_true=hashedEncodedFeatureof_variation['Class'].values We will reuse the same svm functions we defiend earilier svm_model_tuning(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) from the above graph i can choose my C as 0.0001 def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test )) svm_model_testing(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 0.0001 the coresponding train loss is [1.7335822874941234] The Logloss for 0.0001 the coresponding cv loss is [1.753463388538884] The Logloss for 0.0001 the coresponding test loss is [1.7599055604165221] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 3.OneHotencoding of variation feature As this has high dimention lets use logistic regression </div> </div> </div> oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0') oneHotEncodedfeaturesof_Variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns oneHotEncodedfeaturesof_Variation.columns Index(['1_2009trunc', '2010_2471trunc', '256_286trunc', '3' Deletion', '385_418del', '422_605trunc', '533_534del', '534_536del', '550_592del', '560_561insER', ... 'Y901C', 'Y931C', 'Y98H', 'Y98N', 'YAP1-FAM118B Fusion', 'YAP1-MAMLD1 Fusion', 'ZC3H7B-BCOR Fusion', 'ZNF198-FGFR1 Fusion', 'p61BRAF', 'Class'], dtype='object', length=2997) x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values LogisticModel def logistic_tune(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) logistic_tune(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) From the above graph i can choos aplha as 0.01 Testing model def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test )) logistic_test(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) The Logloss for 0.01 the coresponding train loss is [1.4238762684713966] The Logloss for 0.01 the coresponding cv loss is [1.744474382727282] The Logloss for 0.01 the coresponding test loss is [1.7380242209450858] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets compare all of them Lets compare all observations on Variation Feature table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599]) table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479]) table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Variation | FeatureHaser | SVM-kernel-RBF | 0.0001 | 1.7335 | 1.7534 | 1.7599 | | Variation | MeanResponseCoding | SVM-kernel-RBF | 1 | 0.134 | 0.1479 | 0.1479 | | Variation | OneHotEncoding | LogisticRegression | 0.01 | 1.4238 | 1.7444 | 1.738 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well. Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.4 Analysing of Text Feature</p> </div> </div> </div> Q1.What kind of feature is Text </div> </div> </div> It is sentance corpus consisting of many sentances. Q2.how can we featurise it There are many ways we will work with these two 1.BOW 2.TFIDF </div> </div> </div> 1.Featurising with bow df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... bow_df=df bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation']) array_input=bow_df.Cleaned_text.values bow_ds=df vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000) tf=vectoriser.fit(array_input) vocab_countVectoriser=(tf.vocabulary_) vocab_countVectoriser.items() dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 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'concept', 'conclude', 'concluded', 'conclusion', 'conclusions', 'concomitant', 'concordance', 'concurrent', 'condition', 'conditional', 'conditions', 'conducted', 'confer', 'confer resistance', 'conferred', 'conferring', 'confers', 'confidence', 'confirm', 'confirmation', 'confirmed', 'confirming', 'confluence', 'conformation', 'conformational', 'conformational change', 'conformational changes', 'conformations', 'congenital', 'conjugated', 'consecutive', 'consensus', 'consent', 'consequence', 'consequences', 'consequently', 'conservation', 'conservative', 'conserved', 'consider', 'considerable', 'considered', 'considering', 'consisted', 'consistent', 'consistent previous', 'consistently', 'consisting', 'consists', 'consortium', 'constant', 'constitute', 'constitutional', 'constitutive', 'constitutive activation', 'constitutively', 'constitutively activated', 'constitutively active', 'construct', 'constructed', 'construction', 'constructs', 'contact', 'contact help', 'contacts', 'contain', 'contained', 'containing', 'containing mm', 'contains', 'content', 'context', 'contexts', 'continued', 'continuous', 'contrast', 'contribute', 'contributed', 'contributes', 'contributing', 'contribution', 'control', 'control cells', 'controlled', 'controlling', 'controls', 'conventional', 'conversely', 'conversion', 'cooh', 'cooh terminal', 'cooperate', 'cooperative', 'copies', 'copy', 'copy number', 'core', 'core domain', 'core enzyme', 'correct', ...] array_transformed=vectoriser.transform(array_input) array_transformedone=array_transformed.toarray() dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow ) dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1) dataframofBowForText.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aa aacrjournals aacrjournals org ab abbreviations abc abd aberrant aberrant splicing aberrations abi ability ability bind ability induce abl able abnormal abnormalities abolish abolished abrogate abrogated absence absent absolute abstract abundance abundant ac acc accelerated accepted acceptor access access image accessible accessible alternative accession accompanied accordance ... withdrawal within without wm wm cells wnt women work worldwide worse wpd written wt wt mutant wt wt wtb wtb raf wu xenograft xenografts xenopus xl xp xrcc xu yap year years yeast yeast cells yellow yes yet yield yielded young zhang zinc zn Class 0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 ... 0 3 3 0 0 0 0 2 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 3 0 0 0 0 1 0 0 2 0 0 1 1 0 0 3 2 0 8 0 1 0 1 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ... 2 2 5 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 4 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 ... 0 4 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 10 4 5 rows × 5001 columns dataframofBowForText.to_csv("datafraneofBowTextFeature.csv") 2.TFIDF featurisation of text data </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... tfidf_df=df tfidf_ds=df tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class']) tfidf_df .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Cleaned_text 0 cyclin dependent kinases cdks regulate variety... 1 abstract background non small cell lung cancer... 2 abstract background non small cell lung cancer... 3 recent evidence demonstrated acquired uniparen... 4 oncogenic mutations monomeric casitas b lineag... ... ... 3316 introduction myelodysplastic syndromes mds het... 3317 introduction myelodysplastic syndromes mds het... 3318 runt related transcription factor gene runx al... 3319 runx gene frequent target chromosomal transloc... 3320 frequent mutations associated leukemia recurre... 3321 rows × 1 columns array_input_tfidf=tfidf_df['Cleaned_text'].values vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000) vectorizer.fit(array_input_tfidf) TfidfVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.float64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=1000, min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=True, stop_words=None, strip_accents=None, sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True, vocabulary=None) len(vectorizer.vocabulary_) 1000 featurenames_tfidf=vectorizer.get_feature_names() tfidf_vocab=vectorizer.vocabulary_ transformed_tfidf_csr=vectorizer.transform(array_input_tfidf) transformed_tfidf_csr=transformed_tfidf_csr.toarray() arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf) tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1) tfidf_textfeature.to_csv("tfidf_textfeatures.csv") tfidf_textfeature .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional adenocarcinoma advanced affect affected affecting affinity age akt al ala alk allele alleles alone alterations altered alternative although ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 0.000000 0.0 0.000000 0.007996 0.003394 0.003430 0.000000 0.000000 0.000000 0.0 0.0 0.005836 0.000000 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0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 0.000000 0.0 0.000000 0.026962 0.007630 0.000000 0.003861 0.000000 0.000000 0.0 0.0 0.009838 0.018076 0.003339 0.000000 0.000000 0.000000 0.017664 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 0.000000 0.0 0.010418 0.009430 0.007205 0.031548 0.000000 0.000000 0.000000 0.0 0.0 0.008259 0.000000 0.000000 0.000000 0.000000 0.004647 0.006950 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 0.000000 0.0 0.001547 0.000000 0.000000 0.000000 0.004330 0.000000 0.006224 0.0 0.0 0.000000 0.000000 0.013106 0.003385 0.000000 0.000000 0.004953 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 0.000000 0.0 0.001989 0.000000 0.000000 0.000000 0.005570 0.000000 0.008005 0.0 0.0 0.000000 0.000000 0.016859 0.002177 0.000000 0.000000 0.006371 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.003138 0.000000 0.000000 0.005647 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.001402 0.001502 0.002697 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 0.000000 0.0 0.000000 0.018975 0.000000 0.002713 0.005434 0.000000 0.093724 0.0 0.0 0.013848 0.005089 0.011748 0.025491 0.000000 0.023376 0.004662 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 0.000000 0.0 0.003222 0.025663 0.000000 0.009006 0.003006 0.000000 0.055096 0.0 0.0 0.010216 0.004223 0.010400 0.015279 0.000000 0.014371 0.005158 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1001 columns Q3. Is this Bow,tfidf for text feature Useful in our classification? </div> </div> </div> Lets find out by tsne and one simple model. TSNE for BOW TEXT Feature x_std=dataframofBowForText.iloc[:, 0:5000].values y_true_std=dataframofBowForText.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_std) intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df10,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Tsne for TFIDF data x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values y_tfidf_true=tfidf_textfeature.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_tfidf_true) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df12,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Lets do analisys based on models for the two featurisations and compare the results Lets go with simple model like logistic regression for high dimension data 1.BOW Feature for text def oneHot_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) oneHot_Logsitic( data,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data oneHot_Logsitic( x_std,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one is without standardised data from the graphs i can take my alpha as 0.001 Testing on test datasets def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test )) logistic_test(x_std,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0724656346673704] The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368] The Logloss for 0.001 the coresponding test loss is [1.3034431218014317] Above is result on normal data logistic_test(data,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9441582057917238] The Logloss for 0.001 the coresponding cv loss is [1.210222073584811] The Logloss for 0.001 the coresponding test loss is [1.1539667998430334] Above is Result on standardised data we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well. 2.TFIDF feauture for text Using Logisticmodels we bult for previous cases Tuning oneHot_Logsitic(x_tfidf_true,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was without standardised data oneHot_Logsitic(data,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data From the graphs i can select my alpha as 0.001 Lets Test the model without standardised data logistic_test(x_tfidf_true,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0655639055266641] The Logloss for 0.001 the coresponding cv loss is [1.287554414069922] The Logloss for 0.001 the coresponding test loss is [1.2673057224719144] with standardised data logistic_test(data,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9062325983187661] The Logloss for 0.001 the coresponding cv loss is [1.154476481391309] The Logloss for 0.001 the coresponding test loss is [1.2146295572433117] Observations of Text featureised models with Standardised data only</p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 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-0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... Q1. what is the Variation Feature Type? </div> </div> </div> It is Categorical </div> </div> </div> Q2.How many categories are present? </div> </div> </div> len(df.Variation.value_counts()) 2996 There are 2996 categories </div> </div> </div> Q3.What is the distribution of categories? </div> </div> </div> unique_variations=df.Variation.value_counts() sum0f_unique=sum(unique_variations) histoGram_variation=unique_variations.values / sum0f_unique plt.plot(histoGram_variation ,label = "Histogram of Variation") plt.xlabel(" index of Variation") plt.ylabel(" count") plt.legend() plt.grid() plt.show() by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0. cumsum_histogram=np.cumsum(histoGram_variation) plt.plot(cumsum_histogram,label='Cumulative distribution of variations') plt.grid() plt.legend() plt.show() Q4.How to Featurise this variation Feature? </div> </div> </div> There are few ways:- 1.OneHotEncoding 2.FeatureHasher 3.MeanResponseCoding </div> </div> </div> Lets Start with OneHotEncoding of VariationFeature ds_oneHotencoding=df ds_meanResponse_coding=df ds_featureHasher=df ds_oneHotencoding.columns Index(['ID', 'Gene', 'Variation', 'Class', 'Cleaned_text'], dtype='object') ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text']) ds_oneHotencoding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation 0 Truncating Mutations 1 W802* 2 Q249E 3 N454D 4 L399V label_encoder=LabelEncoder() ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"]) ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns featurenames=label_encoder.classes_ ds_oneHotencoding .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Labelencodedvalues 0 Truncating Mutations 2629 1 W802* 2856 2 Q249E 1897 3 N454D 1667 4 L399V 1447 ... ... ... 3316 D171N 306 3317 A122* 28 3318 Fusions 807 3319 R80C 2249 3320 K83E 1333 3321 rows × 2 columns onehotencoder_Variation=OneHotEncoder() encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray() encoded_array array([[0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], ..., [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.], [0., 0., 0., ..., 0., 0., 0.]]) ds_dash=pd.DataFrame(encoded_array , columns=featurenames) ds_dash.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insGLYVDFREYEY Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5 rows × 2996 columns OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1) OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues']) OneHotEncoded_ds.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv") OneHotEncoded_ds .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 3321 rows × 2997 columns Lets create feature hasher for variation features ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"]) ds_featureHasher.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Variation Class 0 Truncating Mutations 1 1 W802* 2 2 Q249E 2 3 N454D 3 4 L399V 4 hasher= FeatureHasher(n_features=9,input_type='string') array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray() ds_dash_hashed=pd.DataFrame(array_hashed) hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1) hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv") Lets go for mean responsecoding of variation feature df_mean_response_coding.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... objects_ThisFeature=df_mean_response_coding.Variation.value_counts() objects_ThisFeature Truncating Mutations 93 Deletion 74 Amplification 71 Fusions 34 Overexpression 6 .. K78A 1 A1789S 1 T401I 1 K550_V555delinsI 1 R420H 1 Name: Variation, Length: 2996, dtype: int64 my_dictionary_varFeature={ } for feature_name , feature_total_count in objects_ThisFeature.items(): vector_array_features=[ ] for index in range(1,10): count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)]) vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90))) my_dictionary_varFeature[feature_name] = vector_array_features my_dictionary_varFeature {'Truncating Mutations': [0.5409836065573771, 0.060109289617486336, 0.0546448087431694, 0.060109289617486336, 0.0546448087431694, 0.06557377049180328, 0.0546448087431694, 0.0546448087431694, 0.0546448087431694], 'Deletion': [0.40853658536585363, 0.06097560975609756, 0.06097560975609756, 0.15853658536585366, 0.06097560975609756, 0.06707317073170732, 0.06097560975609756, 0.06097560975609756, 0.06097560975609756], 'Amplification': [0.062111801242236024, 0.19254658385093168, 0.062111801242236024, 0.062111801242236024, 0.062111801242236024, 0.12422360248447205, 0.3105590062111801, 0.062111801242236024, 0.062111801242236024], 'Fusions': [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], 'Overexpression': [0.10416666666666667, 0.125, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.10416666666666667, 0.14583333333333334, 0.10416666666666667, 0.10416666666666667], 'G12V': [0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.10638297872340426, 0.14893617021276595, 0.10638297872340426, 0.10638297872340426], 'Q61H': [0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011], 'Q61L': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'E17K': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'T58I': [0.10752688172043011, 0.12903225806451613, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.11827956989247312, 0.10752688172043011, 0.10752688172043011], 'Q61R': [0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.10752688172043011, 0.13978494623655913, 0.10752688172043011, 0.10752688172043011], 'P34R': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Q22K': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'S308A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Promoter Hypermethylation': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'F384L': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'E330K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G12C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'Y42C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G67R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G13V': [0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'P130S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'Q209L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'T286A': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G12A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'I31M': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G12S': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'A146V': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'E542K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'TMPRSS2-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'M1R': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'G35R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304], 'Y64A': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R173C': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'ETV6-NTRK3 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'G13C': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R170W': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'G13D': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'K117N': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'C618R': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'F28L': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'Q61K': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S222D': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T73I': [0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'R841K': [0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'V321M': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304], 'T167A': [0.10869565217391304, 0.10869565217391304, 0.11956521739130435, 0.11956521739130435, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304], 'A146T': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'EWSR1-ETV1 Fusion': [0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.10869565217391304, 0.13043478260869565, 0.10869565217391304, 0.10869565217391304], 'S501_A502dup': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R287A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A57V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G309A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'W131G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V555_V559del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R683K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A18D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E239A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E439del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R776C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S241L': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'FGFR1-TACC1 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G42R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T160I': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'ROS1-CD74 Fusion': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R373H': [0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N546K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R465H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V1676D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1686Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I122S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y599_D600insSTDNEYFYVDFREYEY': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1172L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R487W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L838P': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E127G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'L117P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T674I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P278A': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W24C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'X963_splice': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R2336H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A2770T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P81T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S464L': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D331G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'A750_E758del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C482R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S36Y': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V465M': 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0.10989010989010989], 'L424V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V774A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H1094R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], '2010_2471trunc': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D816E': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'P551_V555del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T315I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K292T': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G1128S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T123A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], 'I744_K745delinsKIPVAI': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G81D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'S1841N': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D839G': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H1094Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 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0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C91S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V242F': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'Y553N': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D289_D292del': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K45T': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M774_A775insAYVM': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'DNMT3B7': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989], 'S33Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'G67S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G596C': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'H36P': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'Y652H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'I1018W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V769_D770insGVV': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R282W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V11A': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R420Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E82V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P186S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V155F': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G380R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'C135R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'S35Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T205A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E746_A750del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D83V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K181M': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088], 'GIT2-PDGFRB Fusion': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T417I': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R80P': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R1097H': [0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'I1170S': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R283Q': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E1071W': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P114L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F74S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'R905Q': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'W383R': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'R905W': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N372H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'DNA binding domain deletions': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D473G': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'K550_W557del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'T529N': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H61D': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'M1255I': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'N116H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L747_P753del': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'D408H': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'P480L': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'T1691K': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L1947R': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'E172K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'V344A': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'L248V': [0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G129E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'D408E': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'F119S': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'H597Y': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'EGFRvIII': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'E40K': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989], 'A1752V': [0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], 'G248V': [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 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0.10989010989010989], [0.0967741935483871, 0.33064516129032256, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08064516129032258, 0.08870967741935484, 0.08064516129032258], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989], [0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.12087912087912088, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989, 0.10989010989010989]] meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss) meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1) meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv") As we have all the vector forms of the featurisations lets proceed with further analysis Q5.How good is this Variation feature in predicting y_i? Lets Build simple models to check our selves As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library. 1.meanResponse coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> SVM Model meanResponseCoding_variationFeature.columns Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 'Class'], dtype='object') x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values x_true_meanresponse array([[0.54098361, 0.06010929, 0.05464481, ..., 0.05464481, 0.05464481, 0.05464481], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.12087912, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], ..., [0.09677419, 0.33064516, 0.08064516, ..., 0.08064516, 0.08870968, 0.08064516], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011], [0.10989011, 0.10989011, 0.10989011, ..., 0.10989011, 0.10989011, 0.10989011]]) def svm_model_tuning(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) svm_model_tuning(x_true_meanresponse , y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) From above graph i select my best C as 1 Testing of the model def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) svm_model_testing(x_true_meanresponse,y_true_meanResponse) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 1 the coresponding train loss is [0.13402600125222605] The Logloss for 1 the coresponding cv loss is [0.1479095316786708] The Logloss for 1 the coresponding test loss is [0.12267536305037746] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 2.Hashed coded vectors for variation feature We will use SVM rbf kernel and see the performance </div> </div> </div> hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0') hashedEncodedFeatureof_variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 Class 0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 1 1 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 2 2 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 3 4 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 4 x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values y_hashed_true=hashedEncodedFeatureof_variation['Class'].values We will reuse the same svm functions we defiend earilier svm_model_tuning(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) from the above graph i can choose my C as 0.0001 def svm_model_testing(var1,var2): """ This function is used to create SVM and tune it """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced') model.fit(x_train,y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test )) svm_model_testing(x_hashed_true,y_hashed_true) The shape of the train n test vector as follows: (2124, 9) (2124,) (532, 9) (532,) (665, 9) (665,) The Logloss for 0.0001 the coresponding train loss is [1.7335822874941234] The Logloss for 0.0001 the coresponding cv loss is [1.753463388538884] The Logloss for 0.0001 the coresponding test loss is [1.7599055604165221] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well 3.OneHotencoding of variation feature As this has high dimention lets use logistic regression </div> </div> </div> oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0') oneHotEncodedfeaturesof_Variation.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 1_2009trunc 2010_2471trunc 256_286trunc 3' Deletion 385_418del 422_605trunc 533_534del 534_536del 550_592del 560_561insER 596_619splice 963_D1010splice 981_1028splice A1020V A1022E A1065T A1066V A1099T A111P A1131T A113_splice A1170V A11_G12insGA A1200V A120S A121E A121P A121V A122* A1234T A126D A126G A126S A126V A134D A1374V A1459P A146T A146V A148T ... Y599_D600insPAPQIMSTSTLISENMNIA Y599_D600insSTDNEYFYVDFREYEY Y62C Y63C Y640F Y646C Y646F Y646H Y646N Y646S Y647C Y64A Y652H Y65C Y68D Y68H Y69H Y772_A775dup Y791F Y801H Y803N Y806C Y823D Y835F Y842C Y846C Y849C Y849S Y87C Y87N Y901C Y931C Y98H Y98N YAP1-FAM118B Fusion YAP1-MAMLD1 Fusion ZC3H7B-BCOR Fusion ZNF198-FGFR1 Fusion p61BRAF Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4 5 rows × 2997 columns oneHotEncodedfeaturesof_Variation.columns Index(['1_2009trunc', '2010_2471trunc', '256_286trunc', '3' Deletion', '385_418del', '422_605trunc', '533_534del', '534_536del', '550_592del', '560_561insER', ... 'Y901C', 'Y931C', 'Y98H', 'Y98N', 'YAP1-FAM118B Fusion', 'YAP1-MAMLD1 Fusion', 'ZC3H7B-BCOR Fusion', 'ZNF198-FGFR1 Fusion', 'p61BRAF', 'Class'], dtype='object', length=2997) x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values LogisticModel def logistic_tune(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in hyperparameter: model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) plt.plot(np.log(hyperparameter) , log_loss_train) plt.plot(np.log(hyperparameter), log_loss_cv) plt.scatter(np.log(hyperparameter),log_loss_cv) plt.title("Loggloss vs hyper paramenter") plt.xlabel("hyperparameter(C)") plt.ylabel("Log loss") plt.grid() return(plt.show()) logistic_tune(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) From the above graph i can choos aplha as 0.01 Testing model def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test )) logistic_test(x_onehot_true,y_onehot_true) The shape of the train n test vector as follows: (2124, 2996) (2124,) (532, 2996) (532,) (665, 2996) (665,) The Logloss for 0.01 the coresponding train loss is [1.4238762684713966] The Logloss for 0.01 the coresponding cv loss is [1.744474382727282] The Logloss for 0.01 the coresponding test loss is [1.7380242209450858] we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets compare all of them Lets compare all observations on Variation Feature table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599]) table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479]) table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380]) print(table) +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ | Variation | FeatureHaser | SVM-kernel-RBF | 0.0001 | 1.7335 | 1.7534 | 1.7599 | | Variation | MeanResponseCoding | SVM-kernel-RBF | 1 | 0.134 | 0.1479 | 0.1479 | | Variation | OneHotEncoding | LogisticRegression | 0.01 | 1.4238 | 1.7444 | 1.738 | +--------------+--------------------+--------------------+----------------+----------------+-------------+--------------+ From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well. Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)? Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error. 3.1.4 Analysing of Text Feature</p> </div> </div> </div> Q1.What kind of feature is Text </div> </div> </div> It is sentance corpus consisting of many sentances. Q2.how can we featurise it There are many ways we will work with these two 1.BOW 2.TFIDF </div> </div> </div> 1.Featurising with bow df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... bow_df=df bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation']) array_input=bow_df.Cleaned_text.values bow_ds=df vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000) tf=vectoriser.fit(array_input) vocab_countVectoriser=(tf.vocabulary_) vocab_countVectoriser.items() dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 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'concept', 'conclude', 'concluded', 'conclusion', 'conclusions', 'concomitant', 'concordance', 'concurrent', 'condition', 'conditional', 'conditions', 'conducted', 'confer', 'confer resistance', 'conferred', 'conferring', 'confers', 'confidence', 'confirm', 'confirmation', 'confirmed', 'confirming', 'confluence', 'conformation', 'conformational', 'conformational change', 'conformational changes', 'conformations', 'congenital', 'conjugated', 'consecutive', 'consensus', 'consent', 'consequence', 'consequences', 'consequently', 'conservation', 'conservative', 'conserved', 'consider', 'considerable', 'considered', 'considering', 'consisted', 'consistent', 'consistent previous', 'consistently', 'consisting', 'consists', 'consortium', 'constant', 'constitute', 'constitutional', 'constitutive', 'constitutive activation', 'constitutively', 'constitutively activated', 'constitutively active', 'construct', 'constructed', 'construction', 'constructs', 'contact', 'contact help', 'contacts', 'contain', 'contained', 'containing', 'containing mm', 'contains', 'content', 'context', 'contexts', 'continued', 'continuous', 'contrast', 'contribute', 'contributed', 'contributes', 'contributing', 'contribution', 'control', 'control cells', 'controlled', 'controlling', 'controls', 'conventional', 'conversely', 'conversion', 'cooh', 'cooh terminal', 'cooperate', 'cooperative', 'copies', 'copy', 'copy number', 'core', 'core domain', 'core enzyme', 'correct', ...] array_transformed=vectoriser.transform(array_input) array_transformedone=array_transformed.toarray() dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow ) dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1) dataframofBowForText.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aa aacrjournals aacrjournals org ab abbreviations abc abd aberrant aberrant splicing aberrations abi ability ability bind ability induce abl able abnormal abnormalities abolish abolished abrogate abrogated absence absent absolute abstract abundance abundant ac acc accelerated accepted acceptor access access image accessible accessible alternative accession accompanied accordance ... withdrawal within without wm wm cells wnt women work worldwide worse wpd written wt wt mutant wt wt wtb wtb raf wu xenograft xenografts xenopus xl xp xrcc xu yap year years yeast yeast cells yellow yes yet yield yielded young zhang zinc zn Class 0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 ... 0 3 3 0 0 0 0 2 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 3 0 0 0 0 1 0 0 2 0 0 1 1 0 0 3 2 0 8 0 1 0 1 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ... 2 2 5 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 4 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 ... 0 4 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 10 4 5 rows × 5001 columns dataframofBowForText.to_csv("datafraneofBowTextFeature.csv") 2.TFIDF featurisation of text data </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... tfidf_df=df tfidf_ds=df tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class']) tfidf_df .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Cleaned_text 0 cyclin dependent kinases cdks regulate variety... 1 abstract background non small cell lung cancer... 2 abstract background non small cell lung cancer... 3 recent evidence demonstrated acquired uniparen... 4 oncogenic mutations monomeric casitas b lineag... ... ... 3316 introduction myelodysplastic syndromes mds het... 3317 introduction myelodysplastic syndromes mds het... 3318 runt related transcription factor gene runx al... 3319 runx gene frequent target chromosomal transloc... 3320 frequent mutations associated leukemia recurre... 3321 rows × 1 columns array_input_tfidf=tfidf_df['Cleaned_text'].values vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000) vectorizer.fit(array_input_tfidf) TfidfVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.float64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=1000, min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=True, stop_words=None, strip_accents=None, sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True, vocabulary=None) len(vectorizer.vocabulary_) 1000 featurenames_tfidf=vectorizer.get_feature_names() tfidf_vocab=vectorizer.vocabulary_ transformed_tfidf_csr=vectorizer.transform(array_input_tfidf) transformed_tfidf_csr=transformed_tfidf_csr.toarray() arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf) tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1) tfidf_textfeature.to_csv("tfidf_textfeatures.csv") tfidf_textfeature .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional adenocarcinoma advanced affect affected affecting affinity age akt al ala alk allele alleles alone alterations altered alternative although ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 0.000000 0.0 0.000000 0.007996 0.003394 0.003430 0.000000 0.000000 0.000000 0.0 0.0 0.005836 0.000000 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0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 0.000000 0.0 0.000000 0.026962 0.007630 0.000000 0.003861 0.000000 0.000000 0.0 0.0 0.009838 0.018076 0.003339 0.000000 0.000000 0.000000 0.017664 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 0.000000 0.0 0.010418 0.009430 0.007205 0.031548 0.000000 0.000000 0.000000 0.0 0.0 0.008259 0.000000 0.000000 0.000000 0.000000 0.004647 0.006950 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 0.000000 0.0 0.001547 0.000000 0.000000 0.000000 0.004330 0.000000 0.006224 0.0 0.0 0.000000 0.000000 0.013106 0.003385 0.000000 0.000000 0.004953 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 0.000000 0.0 0.001989 0.000000 0.000000 0.000000 0.005570 0.000000 0.008005 0.0 0.0 0.000000 0.000000 0.016859 0.002177 0.000000 0.000000 0.006371 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.003138 0.000000 0.000000 0.005647 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.001402 0.001502 0.002697 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 0.000000 0.0 0.000000 0.018975 0.000000 0.002713 0.005434 0.000000 0.093724 0.0 0.0 0.013848 0.005089 0.011748 0.025491 0.000000 0.023376 0.004662 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 0.000000 0.0 0.003222 0.025663 0.000000 0.009006 0.003006 0.000000 0.055096 0.0 0.0 0.010216 0.004223 0.010400 0.015279 0.000000 0.014371 0.005158 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1001 columns Q3. Is this Bow,tfidf for text feature Useful in our classification? </div> </div> </div> Lets find out by tsne and one simple model. TSNE for BOW TEXT Feature x_std=dataframofBowForText.iloc[:, 0:5000].values y_true_std=dataframofBowForText.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_std) intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df10,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Tsne for TFIDF data x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values y_tfidf_true=tfidf_textfeature.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_tfidf_true) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df12,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Lets do analisys based on models for the two featurisations and compare the results Lets go with simple model like logistic regression for high dimension data 1.BOW Feature for text def oneHot_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) oneHot_Logsitic( data,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data oneHot_Logsitic( x_std,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one is without standardised data from the graphs i can take my alpha as 0.001 Testing on test datasets def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test )) logistic_test(x_std,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0724656346673704] The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368] The Logloss for 0.001 the coresponding test loss is [1.3034431218014317] Above is result on normal data logistic_test(data,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9441582057917238] The Logloss for 0.001 the coresponding cv loss is [1.210222073584811] The Logloss for 0.001 the coresponding test loss is [1.1539667998430334] Above is Result on standardised data we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well. 2.TFIDF feauture for text Using Logisticmodels we bult for previous cases Tuning oneHot_Logsitic(x_tfidf_true,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was without standardised data oneHot_Logsitic(data,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data From the graphs i can select my alpha as 0.001 Lets Test the model without standardised data logistic_test(x_tfidf_true,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0655639055266641] The Logloss for 0.001 the coresponding cv loss is [1.287554414069922] The Logloss for 0.001 the coresponding test loss is [1.2673057224719144] with standardised data logistic_test(data,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9062325983187661] The Logloss for 0.001 the coresponding cv loss is [1.154476481391309] The Logloss for 0.001 the coresponding test loss is [1.2146295572433117] Observations of Text featureised models with Standardised data only</p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 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-0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- 3.1.4 Analysing of Text Feature</p> </div> </div> </div> Q1.What kind of feature is Text </div> </div> </div> It is sentance corpus consisting of many sentances. Q2.how can we featurise it There are many ways we will work with these two 1.BOW 2.TFIDF </div> </div> </div> 1.Featurising with bow df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... bow_df=df bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation']) array_input=bow_df.Cleaned_text.values bow_ds=df vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000) tf=vectoriser.fit(array_input) vocab_countVectoriser=(tf.vocabulary_) vocab_countVectoriser.items() dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 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'concept', 'conclude', 'concluded', 'conclusion', 'conclusions', 'concomitant', 'concordance', 'concurrent', 'condition', 'conditional', 'conditions', 'conducted', 'confer', 'confer resistance', 'conferred', 'conferring', 'confers', 'confidence', 'confirm', 'confirmation', 'confirmed', 'confirming', 'confluence', 'conformation', 'conformational', 'conformational change', 'conformational changes', 'conformations', 'congenital', 'conjugated', 'consecutive', 'consensus', 'consent', 'consequence', 'consequences', 'consequently', 'conservation', 'conservative', 'conserved', 'consider', 'considerable', 'considered', 'considering', 'consisted', 'consistent', 'consistent previous', 'consistently', 'consisting', 'consists', 'consortium', 'constant', 'constitute', 'constitutional', 'constitutive', 'constitutive activation', 'constitutively', 'constitutively activated', 'constitutively active', 'construct', 'constructed', 'construction', 'constructs', 'contact', 'contact help', 'contacts', 'contain', 'contained', 'containing', 'containing mm', 'contains', 'content', 'context', 'contexts', 'continued', 'continuous', 'contrast', 'contribute', 'contributed', 'contributes', 'contributing', 'contribution', 'control', 'control cells', 'controlled', 'controlling', 'controls', 'conventional', 'conversely', 'conversion', 'cooh', 'cooh terminal', 'cooperate', 'cooperative', 'copies', 'copy', 'copy number', 'core', 'core domain', 'core enzyme', 'correct', ...] array_transformed=vectoriser.transform(array_input) array_transformedone=array_transformed.toarray() dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow ) dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1) dataframofBowForText.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aa aacrjournals aacrjournals org ab abbreviations abc abd aberrant aberrant splicing aberrations abi ability ability bind ability induce abl able abnormal abnormalities abolish abolished abrogate abrogated absence absent absolute abstract abundance abundant ac acc accelerated accepted acceptor access access image accessible accessible alternative accession accompanied accordance ... withdrawal within without wm wm cells wnt women work worldwide worse wpd written wt wt mutant wt wt wtb wtb raf wu xenograft xenografts xenopus xl xp xrcc xu yap year years yeast yeast cells yellow yes yet yield yielded young zhang zinc zn Class 0 0 0 0 1 0 0 0 2 1 0 0 1 0 0 0 0 0 2 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 ... 0 3 3 0 0 0 0 2 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 0 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 1 3 0 0 0 2 0 0 1 0 2 6 0 0 0 0 0 0 0 0 1 0 0 0 0 2 1 0 0 0 0 3 0 0 0 0 0 0 2 3 0 0 0 0 1 0 0 2 0 0 1 1 0 0 3 2 0 8 0 1 0 1 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 ... 2 2 5 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 3 4 0 0 0 1 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 ... 0 4 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 10 4 5 rows × 5001 columns dataframofBowForText.to_csv("datafraneofBowTextFeature.csv") 2.TFIDF featurisation of text data </div> </div> </div> df.head() .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ID Gene Variation Class Cleaned_text 0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety... 1 1 CBL W802* 2 abstract background non small cell lung cancer... 2 2 CBL Q249E 2 abstract background non small cell lung cancer... 3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen... 4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag... tfidf_df=df tfidf_ds=df tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class']) tfidf_df .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } Cleaned_text 0 cyclin dependent kinases cdks regulate variety... 1 abstract background non small cell lung cancer... 2 abstract background non small cell lung cancer... 3 recent evidence demonstrated acquired uniparen... 4 oncogenic mutations monomeric casitas b lineag... ... ... 3316 introduction myelodysplastic syndromes mds het... 3317 introduction myelodysplastic syndromes mds het... 3318 runt related transcription factor gene runx al... 3319 runx gene frequent target chromosomal transloc... 3320 frequent mutations associated leukemia recurre... 3321 rows × 1 columns array_input_tfidf=tfidf_df['Cleaned_text'].values vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000) vectorizer.fit(array_input_tfidf) TfidfVectorizer(analyzer='word', binary=False, decode_error='strict', dtype=<class 'numpy.float64'>, encoding='utf-8', input='content', lowercase=True, max_df=1.0, max_features=1000, min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None, smooth_idf=True, stop_words=None, strip_accents=None, sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b', tokenizer=None, use_idf=True, vocabulary=None) len(vectorizer.vocabulary_) 1000 featurenames_tfidf=vectorizer.get_feature_names() tfidf_vocab=vectorizer.vocabulary_ transformed_tfidf_csr=vectorizer.transform(array_input_tfidf) transformed_tfidf_csr=transformed_tfidf_csr.toarray() arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf) tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1) tfidf_textfeature.to_csv("tfidf_textfeatures.csv") tfidf_textfeature .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional adenocarcinoma advanced affect affected affecting affinity age akt al ala alk allele alleles alone alterations altered alternative although ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 0.000000 0.0 0.000000 0.007996 0.003394 0.003430 0.000000 0.000000 0.000000 0.0 0.0 0.005836 0.000000 0.017823 0.000000 0.000000 0.009851 0.003929 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 0.014053 0.0 0.007465 0.002027 0.002582 0.000000 0.002612 0.002856 0.001877 0.0 0.0 0.006657 0.002446 0.006778 0.016340 0.000000 0.000000 0.004482 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 0.000000 0.0 0.000000 0.026962 0.007630 0.000000 0.003861 0.000000 0.000000 0.0 0.0 0.009838 0.018076 0.003339 0.000000 0.000000 0.000000 0.017664 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 0.000000 0.0 0.010418 0.009430 0.007205 0.031548 0.000000 0.000000 0.000000 0.0 0.0 0.008259 0.000000 0.000000 0.000000 0.000000 0.004647 0.006950 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 0.000000 0.0 0.001547 0.000000 0.000000 0.000000 0.004330 0.000000 0.006224 0.0 0.0 0.000000 0.000000 0.013106 0.003385 0.000000 0.000000 0.004953 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 0.000000 0.0 0.001989 0.000000 0.000000 0.000000 0.005570 0.000000 0.008005 0.0 0.0 0.000000 0.000000 0.016859 0.002177 0.000000 0.000000 0.006371 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.003138 0.000000 0.000000 0.005647 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.001402 0.001502 0.002697 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 0.000000 0.0 0.000000 0.018975 0.000000 0.002713 0.005434 0.000000 0.093724 0.0 0.0 0.013848 0.005089 0.011748 0.025491 0.000000 0.023376 0.004662 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 0.000000 0.0 0.003222 0.025663 0.000000 0.009006 0.003006 0.000000 0.055096 0.0 0.0 0.010216 0.004223 0.010400 0.015279 0.000000 0.014371 0.005158 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1001 columns Q3. Is this Bow,tfidf for text feature Useful in our classification? </div> </div> </div> Lets find out by tsne and one simple model. TSNE for BOW TEXT Feature x_std=dataframofBowForText.iloc[:, 0:5000].values y_true_std=dataframofBowForText.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_std) intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df10,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Tsne for TFIDF data x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values y_tfidf_true=tfidf_textfeature.Class.values stdnd=StandardScaler() data=stdnd.fit_transform(x_tfidf_true) intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0) tsne_data=intialisetsne.fit_transform(data) concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1) sns.set_style("whitegrid") sns.FacetGrid(concatinated_df12,hue='Class',height=5)\ .map(plt.scatter , 'Dim1' , 'Dim2')\ .add_legend() plt.show() we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well Lets do analisys based on models for the two featurisations and compare the results Lets go with simple model like logistic regression for high dimension data 1.BOW Feature for text def oneHot_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) #plt.plot((c), train_auc, label='Train AUC') #plt.plot((c), cv_auc, label='CV AUC') plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV AUC') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) oneHot_Logsitic( data,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data oneHot_Logsitic( x_std,y_true_std) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one is without standardised data from the graphs i can take my alpha as 0.001 Testing on test datasets def logistic_test(var1,var2): """ This function is used to create model and tune it to find best hyper parameter """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test )) logistic_test(x_std,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0724656346673704] The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368] The Logloss for 0.001 the coresponding test loss is [1.3034431218014317] Above is result on normal data logistic_test(data,y_true_std) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9441582057917238] The Logloss for 0.001 the coresponding cv loss is [1.210222073584811] The Logloss for 0.001 the coresponding test loss is [1.1539667998430334] Above is Result on standardised data we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well. 2.TFIDF feauture for text Using Logisticmodels we bult for previous cases Tuning oneHot_Logsitic(x_tfidf_true,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was without standardised data oneHot_Logsitic(data,y_tfidf_true) (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) this one was with standardised data From the graphs i can select my alpha as 0.001 Lets Test the model without standardised data logistic_test(x_tfidf_true,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [1.0655639055266641] The Logloss for 0.001 the coresponding cv loss is [1.287554414069922] The Logloss for 0.001 the coresponding test loss is [1.2673057224719144] with standardised data logistic_test(data,y_tfidf_true) The shape of the train n test vector as follows: (2124, 5000) (2124,) (532, 5000) (532,) (665, 5000) (665,) The Logloss for 0.001 the coresponding train loss is [0.9062325983187661] The Logloss for 0.001 the coresponding cv loss is [1.154476481391309] The Logloss for 0.001 the coresponding test loss is [1.2146295572433117] Observations of Text featureised models with Standardised data only</p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 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-1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 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-0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 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0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 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-0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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-0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 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-0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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(665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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#Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 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-1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p>
</div>
</div>
</div>
Q1.What kind of feature is Text
</div>
</div>
</div>
It is sentance corpus consisting of many sentances.
Q2.how can we featurise it
There are many ways we will work with these two
1.BOW
2.TFIDF
</div>
</div>
</div>
1.Featurising with bow
df.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
bow_df=df
bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])
array_input=bow_df.Cleaned_text.values
bow_ds=df
vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)
tf=vectoriser.fit(array_input)
vocab_countVectoriser=(tf.vocabulary_)
vocab_countVectoriser.items()
dict_items([('cyclin', 1076), ('dependent', 1159), ('kinases', 2421), ('regulate', 3825), ('variety', 4877), ('cellular', 708), ('processes', 3572), ('cdk', 644), ('one', 3189), ('last', 2471), ('activating', 74), ('identified', 2146), ('kinase', 2409), ('activity', 91), ('revealed', 3955), ('previous', 3534), ('work', 4968), ('shown', 4146), ('silencing', 4185), ('increases', 2232), ('et', 1515), ('ets', 1519), ('virus', 4906), ('oncogene', 3182), ('homolog', 2094), ('activation', 77), ('mapk', 2663), ('pathway', 3286), ('confers', 916), ('resistance', 3906), ('breast', 535), ('cancer', 563), ('cells', 676), ('precise', 3501), ('mechanisms', 2721), ('generally', 1894), ('functions', 1838), ('remain', 3848), ('demonstrate', 1147), ('identifying', 2151), ('product', 3576), ('whose', 4951), ('mutations', 2953), ('cause', 631), ('syndrome', 4482), ('human', 2117), ('developmental', 1207), ('features', 1670), ('include', 2221), ('renal', 3859), ('show', 4142), ('associated', 339), ('mutants', 2927), ('unable', 4788), ('interact', 2317), ('increasing', 2233), ('raf', 3710), ('conferring', 915), ('phosphorylates', 3385), ('vitro', 4909), ('positively', 3474), ('controls', 985), ('degradation', 1133), ('proteasome', 3629), ('protein', 3630), ('levels', 2509), ('increased', 2229), ('derived', 1172), ('patient', 3289), ('increase', 2228), ('decreased', 1120), ('results', 3938), ('reveal', 3954), ('additional', 115), ('regulatory', 3832), ('mechanism', 2720), ('plays', 3424), ('key', 2404), ('roles', 3989), ('development', 1206), ('light', 2525), ('molecular', 2857), ('underlying', 4802), ('play', 3421), ('role', 3988), ('control', 981), ('number', 3148), ('genome', 1911), ('contains', 969), ('genes', 1901), ('encoding', 1445), ('proteins', 3644), ('considered', 941), ('members', 2753), ('family', 1656), ('owing', 3235), ('sequence', 4098), ('similarity', 4191), ('known', 2442), ('activated', 71), ('although', 214), ('discovered', 1249), ('almost', 200), ('ago', 154), ('remains', 3851), ('two', 4745), ('without', 4963), ('partner', 3273), ('knowledge', 2441), ('gap', 1856), ('largely', 2467), ('biological', 458), ('act', 65), ('positive', 3470), ('cell', 650), ('cycle', 1072), ('regulator', 3830), ('tumor', 4708), ('suppressor', 4461), ('others', 3219), ('interacts', 2321), ('transcription', 4626), ('factor', 1645), ('inhibits', 2279), ('transcriptional', 4631), ('unknown', 4818), ('knockdown', 2439), ('expression', 1612), ('activates', 73), ('induces', 2258), ('mcf', 2702), ('binding', 439), ('gene', 1884), ('predict', 3504), ('absence', 22), ('truncation', 4698), ('heterozygous', 2053), ('females', 1674), ('pathogenesis', 3279), ('recombinant', 3789), ('heterodimer', 2046), ('active', 85), ('phospho', 3377), ('erk', 1493), ('inducing', 2260), ('estrogen', 1514), ('receptor', 3780), ('er', 1485), ('phosphorylation', 3386), ('finally', 1730), ('detect', 1192), ('level', 2508), ('observed', 3161), ('yeast', 4989), ('hybrid', 2124), ('screen', 4048), ('interaction', 2319), ('signal', 4160), ('mouse', 2879), ('terminal', 4533), ('half', 1999), ('strong', 4373), ('homology', 2097), ('proposed', 3623), ('thus', 4581), ('performed', 3344), ('assays', 327), ('determine', 1198), ('whether', 4945), ('fig', 1692), ('isoform', 2368), ('expressed', 1606), ('produced', 3575), ('phenotype', 3368), ('full', 1815), ('length', 2492), ('detectable', 1193), ('partners', 3274), ('signals', 4168), ('albeit', 174), ('notably', 3113), ('weaker', 4930), ('respectively', 3918), ('tested', 4544), ('different', 1224), ('isoforms', 2369), ('alternative', 210), ('splicing', 4292), ('truncated', 4695), ('corresponding', 1008), ('products', 3578), ('mutated', 2931), ('found', 1787), ('patients', 3292), ('none', 3099), ('shorter', 4141), ('phenotypes', 3369), ('new', 3070), ('window', 4959), ('download', 1326), ('form', 1776), ('complex', 882), ('schematic', 4037), ('representation', 3879), ('analyzed', 245), ('amino', 219), ('acid', 50), ('numbers', 3152), ('indicated', 2246), ('black', 466), ('boxes', 496), ('indicate', 2245), ('internal', 2331), ('deletions', 1146), ('red', 3802), ('box', 495), ('indicates', 2247), ('compared', 866), ('assay', 321), ('set', 4120), ('fusion', 1841), ('dna', 1288), ('domain', 1306), ('interacting', 2318), ('activator', 83), ('empty', 1439), ('plasmids', 3413), ('expressing', 1607), ('lacz', 2457), ('used', 4840), ('reporter', 3874), ('blue', 481), ('region', 3822), ('assayed', 326), ('western', 4940), ('blot', 477), ('analysis', 238), ('myc', 3012), ('wt', 4973), ('kd', 2398), ('transfected', 4641), ('hek', 2025), ('immunoprecipitates', 2194), ('obtained', 3163), ('using', 4844), ('anti', 261), ('antibody', 269), ('correspond', 1007), ('total', 4611), ('lysates', 2619), ('goat', 1959), ('input', 2291), ('corresponds', 1009), ('lower', 2595), ('band', 394), ('upper', 4830), ('panel', 3254), ('endogenous', 1448), ('nonspecific', 3103), ('demonstrated', 1148), ('either', 1416), ('overexpression', 3231), 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'clinical data',
'clinical features',
'clinical information',
'clinical outcome',
'clinical relevance',
'clinical response',
'clinical significance',
'clinical trial',
'clinical trials',
'clinically',
'clinically relevant',
'cll',
'clonal',
'clone',
'cloned',
'clones',
'cloning',
'clontech',
'close',
'closed',
'closely',
'cluster',
'clustered',
'clustering',
'clusters',
'cm',
'cml',
'cmml',
'cmv',
'cns',
'co',
'co expressed',
'co expression',
'co occurrence',
'co transfected',
'coactivator',
'code',
'coding',
'coding region',
'coding sequence',
'codon',
'codons',
'coexpressed',
'coexpression',
'cohort',
'cohorts',
'coil',
'coiled',
'coiled coil',
'cold',
'coli',
'collagen',
'colleagues',
'collected',
'collection',
'collectively',
'colon',
'colon cancer',
'colonies',
'colony',
'colony formation',
'color',
'colorectal',
'colorectal cancer',
'colorectal cancers',
'colored',
'column',
'columns',
'combination',
'combinations',
'combined',
'combining',
'committee',
'common',
'commonly',
'communication',
'comparable',
'comparative',
'compare',
'compared',
'compared cells',
'compared control',
'compared wild',
'compared wt',
'comparing',
'comparison',
'comparisons',
'competent',
'competitive',
'complement',
'complementary',
'complementation',
'complete',
'complete loss',
'completely',
'complex',
'complex formation',
'complexes',
'component',
'components',
'composed',
'compound',
'compounds',
'comprehensive',
'comprise',
'comprised',
'comprises',
'comprising',
'compromised',
'computational',
'computed',
'concentration',
'concentrations',
'concept',
'conclude',
'concluded',
'conclusion',
'conclusions',
'concomitant',
'concordance',
'concurrent',
'condition',
'conditional',
'conditions',
'conducted',
'confer',
'confer resistance',
'conferred',
'conferring',
'confers',
'confidence',
'confirm',
'confirmation',
'confirmed',
'confirming',
'confluence',
'conformation',
'conformational',
'conformational change',
'conformational changes',
'conformations',
'congenital',
'conjugated',
'consecutive',
'consensus',
'consent',
'consequence',
'consequences',
'consequently',
'conservation',
'conservative',
'conserved',
'consider',
'considerable',
'considered',
'considering',
'consisted',
'consistent',
'consistent previous',
'consistently',
'consisting',
'consists',
'consortium',
'constant',
'constitute',
'constitutional',
'constitutive',
'constitutive activation',
'constitutively',
'constitutively activated',
'constitutively active',
'construct',
'constructed',
'construction',
'constructs',
'contact',
'contact help',
'contacts',
'contain',
'contained',
'containing',
'containing mm',
'contains',
'content',
'context',
'contexts',
'continued',
'continuous',
'contrast',
'contribute',
'contributed',
'contributes',
'contributing',
'contribution',
'control',
'control cells',
'controlled',
'controlling',
'controls',
'conventional',
'conversely',
'conversion',
'cooh',
'cooh terminal',
'cooperate',
'cooperative',
'copies',
'copy',
'copy number',
'core',
'core domain',
'core enzyme',
'correct',
...]
array_transformed=vectoriser.transform(array_input)
array_transformedone=array_transformed.toarray()
dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )
dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)
dataframofBowForText.head()
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aa
aacrjournals
aacrjournals org
ab
abbreviations
abc
abd
aberrant
aberrant splicing
aberrations
abi
ability
ability bind
ability induce
abl
able
abnormal
abnormalities
abolish
abolished
abrogate
abrogated
absence
absent
absolute
abstract
abundance
abundant
ac
acc
accelerated
accepted
acceptor
access
access image
accessible
accessible alternative
accession
accompanied
accordance
...
withdrawal
within
without
wm
wm cells
wnt
women
work
worldwide
worse
wpd
written
wt
wt mutant
wt wt
wtb
wtb raf
wu
xenograft
xenografts
xenopus
xl
xp
xrcc
xu
yap
year
years
yeast
yeast cells
yellow
yes
yet
yield
yielded
young
zhang
zinc
zn
Class
0
0
0
0
1
0
0
0
2
1
0
0
1
0
0
0
0
0
2
0
0
0
0
3
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
1
...
0
3
3
0
0
0
0
2
0
0
0
0
10
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
4
0
0
0
2
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0
2
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
...
0
1
3
0
0
0
2
0
0
1
0
2
6
0
0
0
0
0
0
0
0
1
0
0
0
0
2
1
0
0
0
0
3
0
0
0
0
0
0
2
2
0
0
0
0
0
0
0
0
0
0
2
0
0
0
2
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
...
0
1
3
0
0
0
2
0
0
1
0
2
6
0
0
0
0
0
0
0
0
1
0
0
0
0
2
1
0
0
0
0
3
0
0
0
0
0
0
2
3
0
0
0
0
1
0
0
2
0
0
1
1
0
0
3
2
0
8
0
1
0
1
4
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
...
2
2
5
0
0
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
0
0
0
0
3
4
0
0
0
1
0
0
0
0
0
0
0
2
0
0
0
0
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
...
0
4
2
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
10
4
5 rows × 5001 columns
dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")
2.TFIDF featurisation of text data
</div>
</div>
</div>
df.head()
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ID
Gene
Variation
Class
Cleaned_text
0
0
FAM58A
Truncating Mutations
1
cyclin dependent kinases cdks regulate variety...
1
1
CBL
W802*
2
abstract background non small cell lung cancer...
2
2
CBL
Q249E
2
abstract background non small cell lung cancer...
3
3
CBL
N454D
3
recent evidence demonstrated acquired uniparen...
4
4
CBL
L399V
4
oncogenic mutations monomeric casitas b lineag...
tfidf_df=df
tfidf_ds=df
tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])
tfidf_df
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Cleaned_text
0
cyclin dependent kinases cdks regulate variety...
1
abstract background non small cell lung cancer...
2
abstract background non small cell lung cancer...
3
recent evidence demonstrated acquired uniparen...
4
oncogenic mutations monomeric casitas b lineag...
...
...
3316
introduction myelodysplastic syndromes mds het...
3317
introduction myelodysplastic syndromes mds het...
3318
runt related transcription factor gene runx al...
3319
runx gene frequent target chromosomal transloc...
3320
frequent mutations associated leukemia recurre...
3321 rows × 1 columns
array_input_tfidf=tfidf_df['Cleaned_text'].values
vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)
vectorizer.fit(array_input_tfidf)
TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',
dtype=<class 'numpy.float64'>, encoding='utf-8',
input='content', lowercase=True, max_df=1.0, max_features=1000,
min_df=3, ngram_range=(1, 2), norm='l2', preprocessor=None,
smooth_idf=True, stop_words=None, strip_accents=None,
sublinear_tf=False, token_pattern='(?u)\\b\\w\\w+\\b',
tokenizer=None, use_idf=True, vocabulary=None)
len(vectorizer.vocabulary_)
1000
featurenames_tfidf=vectorizer.get_feature_names()
tfidf_vocab=vectorizer.vocabulary_
transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)
transformed_tfidf_csr=transformed_tfidf_csr.toarray()
arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)
tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)
tfidf_textfeature.to_csv("tfidf_textfeatures.csv")
tfidf_textfeature
.dataframe tbody tr th:only-of-type {
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}
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
adenocarcinoma
advanced
affect
affected
affecting
affinity
age
akt
al
ala
alk
allele
alleles
alone
alterations
altered
alternative
although
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
0.007393
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0.028735
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0.005846
1
1
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0.002611
0.002394
0.002114
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0.00000
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0.014053
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0.0
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0.002446
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0.016340
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...
0.007657
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0.034397
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0.013063
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0.002765
0.0
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0.022081
0.000000
0.008978
0.004245
0.005261
0.001651
0.003302
0.004868
0.010767
0.010767
0.003600
0.001588
0.005494
0.013112
0.002496
0.000000
0.006669
2
2
0.000000
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0.002611
0.002394
0.002114
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0.00000
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0.0
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0.003209
0.014053
0.0
0.007465
0.002027
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0.002856
0.001877
0.0
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0.006657
0.002446
0.006778
0.016340
0.000000
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0.004482
...
0.007657
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0.002277
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0.008978
0.004245
0.005261
0.001651
0.003302
0.004868
0.010767
0.010767
0.003600
0.001588
0.005494
0.013112
0.002496
0.000000
0.006669
2
3
0.008309
0.002790
0.015838
0.006590
0.010917
0.002684
0.002426
0.005995
0.058335
0.003859
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0.017664
...
0.000000
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0.007194
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0.036367
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0.004694
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0.000000
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3
4
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0.002068
4
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
3316
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...
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4
3317
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4
3321 rows × 1001 columns
Q3. Is this Bow,tfidf for text feature Useful in our classification?
</div>
</div>
</div>
Lets find out by tsne and one simple model.
TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values
y_true_std=dataframofBowForText.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)
intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Tsne for TFIDF data
x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values
y_tfidf_true=tfidf_textfeature.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Lets do analisys based on models for the two featurisations and compare the results
Lets go with simple model like logistic regression for high dimension data
1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
oneHot_Logsitic( data,y_true_std)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one was with standardised data
oneHot_Logsitic( x_std,y_true_std)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one is without standardised data
from the graphs i can take my alpha as 0.001
Testing on test datasets
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))
logistic_test(x_std,y_true_std)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [1.0724656346673704]
The Logloss for 0.001 the coresponding cv loss is [1.2723948073040368]
The Logloss for 0.001 the coresponding test loss is [1.3034431218014317]
Above is result on normal data
logistic_test(data,y_true_std)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [0.9441582057917238]
The Logloss for 0.001 the coresponding cv loss is [1.210222073584811]
The Logloss for 0.001 the coresponding test loss is [1.1539667998430334]
Above is Result on standardised data
we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective
Lets look at other featurisations as well.
2.TFIDF feauture for text
Using Logisticmodels we bult for previous cases
Tuning
oneHot_Logsitic(x_tfidf_true,y_tfidf_true)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one was without standardised data
oneHot_Logsitic(data,y_tfidf_true)
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
this one was with standardised data
From the graphs i can select my alpha as 0.001
Lets Test the model
without standardised data
logistic_test(x_tfidf_true,y_tfidf_true)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [1.0655639055266641]
The Logloss for 0.001 the coresponding cv loss is [1.287554414069922]
The Logloss for 0.001 the coresponding test loss is [1.2673057224719144]
with standardised data
logistic_test(data,y_tfidf_true)
The shape of the train n test vector as follows:
(2124, 5000) (2124,)
(532, 5000) (532,)
(665, 5000) (665,)
The Logloss for 0.001 the coresponding train loss is [0.9062325983187661]
The Logloss for 0.001 the coresponding cv loss is [1.154476481391309]
The Logloss for 0.001 the coresponding test loss is [1.2146295572433117]
Observations of Text featureised models with Standardised data only</p>
</div>
</div>
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss |
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
| Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 |
| Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 |
+--------------+--------------------+----------+----------------+----------------+-------------+--------------+
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div>
</div>
</div>
Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div>
</div>
</div>
4.0 Modelling </p>
</div>
</div>
</div>
4.1 Creating Miscellenous Functions
Confusion Matrix,Precision,Recall</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object')
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid',
'acids', 'acquired', 'across',
...
'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt',
'years', 'yeast', 'yet'],
dtype='object', length=1000)
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
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3.449077
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0.167715
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0.098086
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-0.160639
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-0.280409
0.777698
2
2
-0.280827
-0.167630
0.464136
0.727786
0.075276
-0.203730
-0.672016
0.484531
0.416477
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0.290503
0.272057
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...
0.358525
-0.246247
-0.244573
-0.695099
-0.687776
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4
3321 rows × 1019 columns
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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3
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4
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3321 rows × 4261 columns
3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
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3321 rows × 1019 columns
Now we have our data sets lets perform modelling.
Datasets info:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model</p>
</div>
</div>
</div>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
The hyper parameter and logloss for Train data are :1 and [0.9633295236937504]
The hyper parameter and logloss for Train data are :1 and [1.1924015413394058]
The hyper parameter and logloss for Train data are :1 and [1.1982778811800479]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
This is the Base Line Model Results it performed well.
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
withoutstd_gene_var_text_meanTdidf
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4
3321 rows × 1019 columns
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
The hyper parameter and logloss for Train data are :1 and [1.0383832549539975]
The hyper parameter and logloss for Train data are :1 and [1.2612556656065605]
The hyper parameter and logloss for Train data are :1 and [1.2320081611414992]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.2 Knn Model</p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
Counter({1: 917,
2: 921,
3: 945,
4: 926,
5: 932,
6: 941,
7: 913,
8: 953,
9: 953})
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :31 and [0.813976747977174]
The hyper parameter and logloss for cv data are :31 and [0.8788933809062333]
The hyper parameter and logloss for Test data are :31 and [0.8810438075247502]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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0
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
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3316
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4
3321 rows × 1019 columns
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
balancing class using SMOTE
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Counter({1: 921,
2: 926,
3: 949,
4: 926,
5: 932,
6: 944,
7: 919,
8: 953,
9: 953})
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :31 and [0.6814415244209885]
The hyper parameter and logloss for cv data are :31 and [0.7016829214824364]
The hyper parameter and logloss for Test data are :31 and [0.7779077172643827]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
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yeast
yet
Class
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4
3321 rows × 1019 columns
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
Balancing using smote
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Counter({1: 915,
2: 916,
3: 944,
4: 923,
5: 924,
6: 938,
7: 907,
8: 953,
9: 953})
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :15 and [0.6474458212209986]
The hyper parameter and logloss for cv data are :15 and [0.7512635680512498]
The hyper parameter and logloss for Test data are :31 and [0.7368556387957963]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.3 Logistic Regression</p>
</div>
</div>
</div>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
The shape of the train n test vector as follows:
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logloss for 1 the coresponding train loss is [0.7850156568089277]
The Logloss for 1 the coresponding cv loss is [1.1961412442201744]
The Logloss for 1 the coresponding test loss is [1.1748189424714095]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
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...
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0.167715
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0.777698
2
3
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0.727786
0.075276
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0.484531
0.416477
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1.077846
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0.228389
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0.495499
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0.153029
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0.518255
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0.419256
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...
-0.409008
1.153278
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3
4
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0.727786
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0.327926
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...
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-0.219152
4
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
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...
3316
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4
3321 rows × 1019 columns
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.5847329559769139]
The Logloss for 1 the coresponding cv loss is [0.8572068041315724]
The Logloss for 1 the coresponding test loss is [0.8214264881831812]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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...
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...
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4
3321 rows × 1019 columns
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.8735667598121121]
The Logloss for 1 the coresponding cv loss is [1.1928582990002885]
The Logloss for 1 the coresponding test loss is [1.1999154339757783]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.4 SVM </p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
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ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
0.0
0.0
0.0
0.0
0.0
0.0
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4
3321 rows × 4261 columns
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logg loss for training data with best aplha 0.1 is [0.9361514695402671]
The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932]
The Logg loss for test data with best aplha 0.1 is [1.1689839246304874]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.8782130606666145]
The Logg loss for cv data with best aplha 1 is [1.104589994061247]
The Logg loss for test data with best aplha 1 is [1.0574913033666131]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
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4
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3316
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4
3321 rows × 1019 columns
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.9725367454691902]
The Logg loss for cv data with best aplha 1 is [1.1331135752614927]
The Logg loss for test data with best aplha 1 is [1.2037472465904475]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.5 Random Forest</p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 4261 columns
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 1.2606812233244562
Log Loss train: 1.1374431015135666
for n_estimators = 100 and max depth = 5
Log Loss cv: 1.1653337133462423
Log Loss train: 0.9371070459846149
for n_estimators = 100 and max depth = 7
Log Loss cv: 1.12209548509024
Log Loss train: 0.7363291411007856
for n_estimators = 100 and max depth = 10
Log Loss cv: 1.10268480641603
Log Loss train: 0.5519688514792611
for n_estimators = 200 and max depth = 3
Log Loss cv: 1.248631824458489
Log Loss train: 1.1184306206599595
for n_estimators = 200 and max depth = 5
Log Loss cv: 1.1564275473529837
Log Loss train: 0.919144190096995
for n_estimators = 200 and max depth = 7
Log Loss cv: 1.110794380130624
Log Loss train: 0.7207481407448507
for n_estimators = 200 and max depth = 10
Log Loss cv: 1.0961422234706304
Log Loss train: 0.5423272076360501
for n_estimators = 500 and max depth = 3
Log Loss cv: 1.2478678013942157
Log Loss train: 1.114370022235754
for n_estimators = 500 and max depth = 5
Log Loss cv: 1.148009893446
Log Loss train: 0.9050771353940641
for n_estimators = 500 and max depth = 7
Log Loss cv: 1.1026865237241201
Log Loss train: 0.7101699614090803
for n_estimators = 500 and max depth = 10
Log Loss cv: 1.0917480510399207
Log Loss train: 0.5373319633612905
for n_estimators = 1000 and max depth = 3
Log Loss cv: 1.2414453238075647
Log Loss train: 1.1053160288591364
for n_estimators = 1000 and max depth = 5
Log Loss cv: 1.1452379981760221
Log Loss train: 0.900926228080795
for n_estimators = 1000 and max depth = 7
Log Loss cv: 1.0990520521977565
Log Loss train: 0.7050443392267894
for n_estimators = 1000 and max depth = 10
Log Loss cv: 1.0908700848502797
Log Loss train: 0.5357777692154363
for n_estimators = 2000 and max depth = 3
Log Loss cv: 1.240682648359384
Log Loss train: 1.1036497793577018
for n_estimators = 2000 and max depth = 5
Log Loss cv: 1.1411916861993632
Log Loss train: 0.8950347894321122
for n_estimators = 2000 and max depth = 7
Log Loss cv: 1.0971090158549015
Log Loss train: 0.7018660431272183
for n_estimators = 2000 and max depth = 10
Log Loss cv: 1.0925882642141658
Log Loss train: 0.5355792048858152
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
Log Loss cv: 1.11768909971263
Log Loss train: 0.9002694349418067
Log Loss test: 1.1477524391499037
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
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...
...
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...
...
...
...
...
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...
...
...
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4
3321 rows × 1019 columns
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 0.23570046546599333
Log Loss train: 0.19363398591104833
for n_estimators = 100 and max depth = 5
Log Loss cv: 0.22955561081586118
Log Loss train: 0.16166542392105412
for n_estimators = 100 and max depth = 7
Log Loss cv: 0.23627364969006484
Log Loss train: 0.13622754279491436
for n_estimators = 100 and max depth = 10
Log Loss cv: 0.23299169854143326
Log Loss train: 0.09219438260645411
for n_estimators = 200 and max depth = 3
Log Loss cv: 0.15347165131328178
Log Loss train: 0.1289817833963096
for n_estimators = 200 and max depth = 5
Log Loss cv: 0.18867692478728829
Log Loss train: 0.1333800470656138
for n_estimators = 200 and max depth = 7
Log Loss cv: 0.19812746725522537
Log Loss train: 0.11363125813131297
for n_estimators = 200 and max depth = 10
Log Loss cv: 0.2073843284191654
Log Loss train: 0.08264026371353654
for n_estimators = 500 and max depth = 3
Log Loss cv: 0.14838448934316534
Log Loss train: 0.12796172100789224
for n_estimators = 500 and max depth = 5
Log Loss cv: 0.1732893968732289
Log Loss train: 0.1257528101073332
for n_estimators = 500 and max depth = 7
Log Loss cv: 0.18502788808193749
Log Loss train: 0.1059774981029388
for n_estimators = 500 and max depth = 10
Log Loss cv: 0.20316074184161176
Log Loss train: 0.0820099522892585
for n_estimators = 1000 and max depth = 3
Log Loss cv: 0.15096173695782245
Log Loss train: 0.13021826710995227
for n_estimators = 1000 and max depth = 5
Log Loss cv: 0.17583691544250576
Log Loss train: 0.12659716596839768
for n_estimators = 1000 and max depth = 7
Log Loss cv: 0.1862603932648018
Log Loss train: 0.10603952186939303
for n_estimators = 1000 and max depth = 10
Log Loss cv: 0.20453842968817895
Log Loss train: 0.08168228699376241
for n_estimators = 2000 and max depth = 3
Log Loss cv: 0.14749154103551826
Log Loss train: 0.1272450725093758
for n_estimators = 2000 and max depth = 5
Log Loss cv: 0.16918496782525078
Log Loss train: 0.12115201697127558
for n_estimators = 2000 and max depth = 7
Log Loss cv: 0.1820690821751895
Log Loss train: 0.10282294620866399
for n_estimators = 2000 and max depth = 10
Log Loss cv: 0.1984320940418906
Log Loss train: 0.07940530139460128
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 0.16118368189224744
Log Loss train: 0.1295171475728342
Log Loss test: 0.17283674779043046
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 1.2562525882383961
Log Loss train: 1.1495093667153717
for n_estimators = 100 and max depth = 5
Log Loss cv: 1.1414141205937414
Log Loss train: 0.9137821932641781
for n_estimators = 100 and max depth = 7
Log Loss cv: 1.0891671228800952
Log Loss train: 0.7274585518989484
for n_estimators = 100 and max depth = 10
Log Loss cv: 1.0602735362314917
Log Loss train: 0.5419761320891583
for n_estimators = 200 and max depth = 3
Log Loss cv: 1.2256031707183694
Log Loss train: 1.1125457127985048
for n_estimators = 200 and max depth = 5
Log Loss cv: 1.1239819425283584
Log Loss train: 0.8863029989503483
for n_estimators = 200 and max depth = 7
Log Loss cv: 1.0779197694234626
Log Loss train: 0.7025060768309693
for n_estimators = 200 and max depth = 10
Log Loss cv: 1.0544180795539924
Log Loss train: 0.5270883620171445
for n_estimators = 500 and max depth = 3
Log Loss cv: 1.207676966917158
Log Loss train: 1.1036352816983548
for n_estimators = 500 and max depth = 5
Log Loss cv: 1.1147682551650802
Log Loss train: 0.8732109339573471
for n_estimators = 500 and max depth = 7
Log Loss cv: 1.0736966943880142
Log Loss train: 0.6951080822600649
for n_estimators = 500 and max depth = 10
Log Loss cv: 1.0509723046279644
Log Loss train: 0.522813716084056
for n_estimators = 1000 and max depth = 3
Log Loss cv: 1.203560840318964
Log Loss train: 1.100936649316696
for n_estimators = 1000 and max depth = 5
Log Loss cv: 1.1104963322013828
Log Loss train: 0.8689259823733123
for n_estimators = 1000 and max depth = 7
Log Loss cv: 1.0701797377723286
Log Loss train: 0.6895920716141755
for n_estimators = 1000 and max depth = 10
Log Loss cv: 1.0502619913750308
Log Loss train: 0.5205027839317523
for n_estimators = 2000 and max depth = 3
Log Loss cv: 1.2024191406698308
Log Loss train: 1.1001196279920487
for n_estimators = 2000 and max depth = 5
Log Loss cv: 1.1083923280407713
Log Loss train: 0.8684530548880646
for n_estimators = 2000 and max depth = 7
Log Loss cv: 1.0670849255877433
Log Loss train: 0.687383236130885
for n_estimators = 2000 and max depth = 10
Log Loss cv: 1.049339910790633
Log Loss train: 0.518731457270126
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 1.12511787673414
Log Loss train: 0.8844410482107985
Log Loss test: 1.1406196373627946
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.6 Lets apply Stacking classifier</p>
</div>
</div>
</div>
Applying Stacking Classifer on MeanResponse Coding
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)
Log loss (train) on the stacking classifier : 0.14030289465971651
Log loss (cv) on the stacking classifier : 0.2420519229697048
Log loss (train) on the stacking classifier : 0.25972844103712256
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)
for meta classifier i can take my c values as 0.1
Applyin stacking for onehotfeatures
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-4ee702f64944> in <module>()
----> 1 stackingClassifier_tunning(xres,yres)
NameError: name 'stackingClassifier_tunning' is not defined
taking c as 1
stackingClassifier(xres,yres)
(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.30903590626810395
Log loss (cv) on the stacking classifier : 0.5317070749588001
Log loss (train) on the stacking classifier : 0.5271830152079766
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
taking c as 0.1
stackingClassifier(xres,yres)
(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.31039276656264053
Log loss (cv) on the stacking classifier : 0.5340512149631327
Log loss (train) on the stacking classifier : 0.5269476489328632
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
Applyin stacking for hashed Featurisation
std_gene_var_text_hashingtfidf
.dataframe tbody tr th:only-of-type {
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vertical-align: top;
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
stackingClassifier_tunning(xres,yres)
(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)
Log loss (train) on the stacking classifier : 0.31306871841068806
Log loss (cv) on the stacking classifier : 0.5266414657786868
Log loss (train) on the stacking classifier : 0.5249250625775747
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
5.0 Final Observations Preetytableformat</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
| Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 |
| Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 |
| gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 |
| gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 |
| gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 |
| gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 |
| gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 |
| gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 |
| gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 |
| gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
- Observations of Text featureised models with Standardised data only</p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 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-0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 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0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 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0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 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smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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-0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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-0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153]) table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214]) print(table) +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | +--------------+--------------------+----------+----------------+----------------+-------------+--------------+ TFIDF Featurisation is more sensible comparitively than BOW featurisation Q4.Are these Features stable accross all data sets? </div> </div> </div> Yes,because there is no much difference in cv n test log loss. Now we have all the vectors lets go for modelling </div> </div> </div> 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 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-0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 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0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 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0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 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smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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-0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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-0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- 4.0 Modelling </p> </div> </div> </div> 4.1 Creating Miscellenous Functions Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 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-0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 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0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 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0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 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smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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-0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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-0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p>
</div>
</div>
</div>
4.1 Creating Miscellenous Functions
Confusion Matrix,Precision,Recall</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object')
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid',
'acids', 'acquired', 'across',
...
'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt',
'years', 'yeast', 'yet'],
dtype='object', length=1000)
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
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...
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2
2
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...
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2
3
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0.153029
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0.518255
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0.419256
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-0.123545
...
-0.409008
1.153278
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-0.695099
-0.736630
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-0.455797
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3
4
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0.076358
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1.133791
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0.327926
-0.212612
-0.968967
-0.409365
...
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0.264175
0.321014
-0.254817
-0.477152
-0.428174
1.160840
0.459758
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-0.219152
4
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3316
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4
3321 rows × 1019 columns
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
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ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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...
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4
3321 rows × 4261 columns
3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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3321 rows × 1019 columns
Now we have our data sets lets perform modelling.
Datasets info:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model</p>
</div>
</div>
</div>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
The hyper parameter and logloss for Train data are :1 and [0.9633295236937504]
The hyper parameter and logloss for Train data are :1 and [1.1924015413394058]
The hyper parameter and logloss for Train data are :1 and [1.1982778811800479]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
This is the Base Line Model Results it performed well.
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
withoutstd_gene_var_text_meanTdidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
The hyper parameter and logloss for Train data are :1 and [1.0383832549539975]
The hyper parameter and logloss for Train data are :1 and [1.2612556656065605]
The hyper parameter and logloss for Train data are :1 and [1.2320081611414992]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.2 Knn Model</p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37})
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
Counter({1: 917,
2: 921,
3: 945,
4: 926,
5: 932,
6: 941,
7: 913,
8: 953,
9: 953})
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :31 and [0.813976747977174]
The hyper parameter and logloss for cv data are :31 and [0.8788933809062333]
The hyper parameter and logloss for Test data are :31 and [0.8810438075247502]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
.dataframe tbody tr th:only-of-type {
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}
.dataframe tbody tr th {
vertical-align: top;
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
balancing class using SMOTE
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Counter({1: 921,
2: 926,
3: 949,
4: 926,
5: 932,
6: 944,
7: 919,
8: 953,
9: 953})
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :31 and [0.6814415244209885]
The hyper parameter and logloss for cv data are :31 and [0.7016829214824364]
The hyper parameter and logloss for Test data are :31 and [0.7779077172643827]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
.dataframe tbody tr th:only-of-type {
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}
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
...
...
...
...
...
...
...
...
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...
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...
3316
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4
3321 rows × 1019 columns
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
Balancing using smote
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Counter({1: 915,
2: 916,
3: 944,
4: 923,
5: 924,
6: 938,
7: 907,
8: 953,
9: 953})
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
The hyper parameter and logloss for Train data are :15 and [0.6474458212209986]
The hyper parameter and logloss for cv data are :15 and [0.7512635680512498]
The hyper parameter and logloss for Test data are :31 and [0.7368556387957963]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.3 Logistic Regression</p>
</div>
</div>
</div>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
The shape of the train n test vector as follows:
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logloss for 1 the coresponding train loss is [0.7850156568089277]
The Logloss for 1 the coresponding cv loss is [1.1961412442201744]
The Logloss for 1 the coresponding test loss is [1.1748189424714095]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
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}
0
1
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
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4
3321 rows × 1019 columns
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.5847329559769139]
The Logloss for 1 the coresponding cv loss is [0.8572068041315724]
The Logloss for 1 the coresponding test loss is [0.8214264881831812]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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0
1
2
3
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5
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7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
The shape of the train n test vector as follows:
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logloss for 1 the coresponding train loss is [0.8735667598121121]
The Logloss for 1 the coresponding cv loss is [1.1928582990002885]
The Logloss for 1 the coresponding test loss is [1.1999154339757783]
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.4 SVM </p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
.dataframe tbody tr th:only-of-type {
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ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 4261 columns
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
The Logg loss for training data with best aplha 0.1 is [0.9361514695402671]
The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932]
The Logg loss for test data with best aplha 0.1 is [1.1689839246304874]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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...
0.358525
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0.167715
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0.777698
2
3
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0.464136
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0.075276
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0.484531
0.416477
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1.077846
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0.228389
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0.419256
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1.153278
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3
4
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4
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
...
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...
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...
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...
...
...
...
...
...
...
...
...
...
...
...
...
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...
...
...
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...
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...
...
...
...
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...
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...
...
...
...
...
...
3316
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0.272057
0.728993
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4
3321 rows × 1019 columns
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.8782130606666145]
The Logg loss for cv data with best aplha 1 is [1.104589994061247]
The Logg loss for test data with best aplha 1 is [1.0574913033666131]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
0
1
2
3
4
5
6
7
8
0
1
2
3
4
5
6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
0
0.0
2.0
0.0
0.0
1.0
0.0
0.0
0.0
-1.0
-1.0
-1.0
0.0
-8.0
5.0
0.0
0.0
0.0
1.0
0.400946
-0.430196
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4
3321 rows × 1019 columns
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
The Logg loss for training data with best aplha 1 is [0.9725367454691902]
The Logg loss for cv data with best aplha 1 is [1.1331135752614927]
The Logg loss for test data with best aplha 1 is [1.2037472465904475]
-------------------- Confusion matrix --------------------
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.5 Random Forest</p>
DatasetsUsedHere:-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
ABL1
ACVR1
AGO2
AKT1
AKT2
AKT3
ALK
APC
AR
ARAF
ARID1A
ARID1B
ARID2
ARID5B
ASXL1
ASXL2
ATM
ATR
ATRX
AURKA
AURKB
AXIN1
AXL
B2M
BAP1
BARD1
BCL10
BCL2
BCL2L11
BCOR
BRAF
BRCA1
BRCA2
BRD4
BRIP1
BTK
CARD11
CARM1
CASP8
CBL
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 4261 columns
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 1.2606812233244562
Log Loss train: 1.1374431015135666
for n_estimators = 100 and max depth = 5
Log Loss cv: 1.1653337133462423
Log Loss train: 0.9371070459846149
for n_estimators = 100 and max depth = 7
Log Loss cv: 1.12209548509024
Log Loss train: 0.7363291411007856
for n_estimators = 100 and max depth = 10
Log Loss cv: 1.10268480641603
Log Loss train: 0.5519688514792611
for n_estimators = 200 and max depth = 3
Log Loss cv: 1.248631824458489
Log Loss train: 1.1184306206599595
for n_estimators = 200 and max depth = 5
Log Loss cv: 1.1564275473529837
Log Loss train: 0.919144190096995
for n_estimators = 200 and max depth = 7
Log Loss cv: 1.110794380130624
Log Loss train: 0.7207481407448507
for n_estimators = 200 and max depth = 10
Log Loss cv: 1.0961422234706304
Log Loss train: 0.5423272076360501
for n_estimators = 500 and max depth = 3
Log Loss cv: 1.2478678013942157
Log Loss train: 1.114370022235754
for n_estimators = 500 and max depth = 5
Log Loss cv: 1.148009893446
Log Loss train: 0.9050771353940641
for n_estimators = 500 and max depth = 7
Log Loss cv: 1.1026865237241201
Log Loss train: 0.7101699614090803
for n_estimators = 500 and max depth = 10
Log Loss cv: 1.0917480510399207
Log Loss train: 0.5373319633612905
for n_estimators = 1000 and max depth = 3
Log Loss cv: 1.2414453238075647
Log Loss train: 1.1053160288591364
for n_estimators = 1000 and max depth = 5
Log Loss cv: 1.1452379981760221
Log Loss train: 0.900926228080795
for n_estimators = 1000 and max depth = 7
Log Loss cv: 1.0990520521977565
Log Loss train: 0.7050443392267894
for n_estimators = 1000 and max depth = 10
Log Loss cv: 1.0908700848502797
Log Loss train: 0.5357777692154363
for n_estimators = 2000 and max depth = 3
Log Loss cv: 1.240682648359384
Log Loss train: 1.1036497793577018
for n_estimators = 2000 and max depth = 5
Log Loss cv: 1.1411916861993632
Log Loss train: 0.8950347894321122
for n_estimators = 2000 and max depth = 7
Log Loss cv: 1.0971090158549015
Log Loss train: 0.7018660431272183
for n_estimators = 2000 and max depth = 10
Log Loss cv: 1.0925882642141658
Log Loss train: 0.5355792048858152
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
(2124, 4260) (2124,)
(532, 4260) (532,)
(665, 4260) (665,)
Log Loss cv: 1.11768909971263
Log Loss train: 0.9002694349418067
Log Loss test: 1.1477524391499037
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
std_gene_var_text_meanTdidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 0.23570046546599333
Log Loss train: 0.19363398591104833
for n_estimators = 100 and max depth = 5
Log Loss cv: 0.22955561081586118
Log Loss train: 0.16166542392105412
for n_estimators = 100 and max depth = 7
Log Loss cv: 0.23627364969006484
Log Loss train: 0.13622754279491436
for n_estimators = 100 and max depth = 10
Log Loss cv: 0.23299169854143326
Log Loss train: 0.09219438260645411
for n_estimators = 200 and max depth = 3
Log Loss cv: 0.15347165131328178
Log Loss train: 0.1289817833963096
for n_estimators = 200 and max depth = 5
Log Loss cv: 0.18867692478728829
Log Loss train: 0.1333800470656138
for n_estimators = 200 and max depth = 7
Log Loss cv: 0.19812746725522537
Log Loss train: 0.11363125813131297
for n_estimators = 200 and max depth = 10
Log Loss cv: 0.2073843284191654
Log Loss train: 0.08264026371353654
for n_estimators = 500 and max depth = 3
Log Loss cv: 0.14838448934316534
Log Loss train: 0.12796172100789224
for n_estimators = 500 and max depth = 5
Log Loss cv: 0.1732893968732289
Log Loss train: 0.1257528101073332
for n_estimators = 500 and max depth = 7
Log Loss cv: 0.18502788808193749
Log Loss train: 0.1059774981029388
for n_estimators = 500 and max depth = 10
Log Loss cv: 0.20316074184161176
Log Loss train: 0.0820099522892585
for n_estimators = 1000 and max depth = 3
Log Loss cv: 0.15096173695782245
Log Loss train: 0.13021826710995227
for n_estimators = 1000 and max depth = 5
Log Loss cv: 0.17583691544250576
Log Loss train: 0.12659716596839768
for n_estimators = 1000 and max depth = 7
Log Loss cv: 0.1862603932648018
Log Loss train: 0.10603952186939303
for n_estimators = 1000 and max depth = 10
Log Loss cv: 0.20453842968817895
Log Loss train: 0.08168228699376241
for n_estimators = 2000 and max depth = 3
Log Loss cv: 0.14749154103551826
Log Loss train: 0.1272450725093758
for n_estimators = 2000 and max depth = 5
Log Loss cv: 0.16918496782525078
Log Loss train: 0.12115201697127558
for n_estimators = 2000 and max depth = 7
Log Loss cv: 0.1820690821751895
Log Loss train: 0.10282294620866399
for n_estimators = 2000 and max depth = 10
Log Loss cv: 0.1984320940418906
Log Loss train: 0.07940530139460128
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 0.16118368189224744
Log Loss train: 0.1295171475728342
Log Loss test: 0.17283674779043046
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_hashingtfidf
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aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
for n_estimators = 100 and max depth = 3
Log Loss cv: 1.2562525882383961
Log Loss train: 1.1495093667153717
for n_estimators = 100 and max depth = 5
Log Loss cv: 1.1414141205937414
Log Loss train: 0.9137821932641781
for n_estimators = 100 and max depth = 7
Log Loss cv: 1.0891671228800952
Log Loss train: 0.7274585518989484
for n_estimators = 100 and max depth = 10
Log Loss cv: 1.0602735362314917
Log Loss train: 0.5419761320891583
for n_estimators = 200 and max depth = 3
Log Loss cv: 1.2256031707183694
Log Loss train: 1.1125457127985048
for n_estimators = 200 and max depth = 5
Log Loss cv: 1.1239819425283584
Log Loss train: 0.8863029989503483
for n_estimators = 200 and max depth = 7
Log Loss cv: 1.0779197694234626
Log Loss train: 0.7025060768309693
for n_estimators = 200 and max depth = 10
Log Loss cv: 1.0544180795539924
Log Loss train: 0.5270883620171445
for n_estimators = 500 and max depth = 3
Log Loss cv: 1.207676966917158
Log Loss train: 1.1036352816983548
for n_estimators = 500 and max depth = 5
Log Loss cv: 1.1147682551650802
Log Loss train: 0.8732109339573471
for n_estimators = 500 and max depth = 7
Log Loss cv: 1.0736966943880142
Log Loss train: 0.6951080822600649
for n_estimators = 500 and max depth = 10
Log Loss cv: 1.0509723046279644
Log Loss train: 0.522813716084056
for n_estimators = 1000 and max depth = 3
Log Loss cv: 1.203560840318964
Log Loss train: 1.100936649316696
for n_estimators = 1000 and max depth = 5
Log Loss cv: 1.1104963322013828
Log Loss train: 0.8689259823733123
for n_estimators = 1000 and max depth = 7
Log Loss cv: 1.0701797377723286
Log Loss train: 0.6895920716141755
for n_estimators = 1000 and max depth = 10
Log Loss cv: 1.0502619913750308
Log Loss train: 0.5205027839317523
for n_estimators = 2000 and max depth = 3
Log Loss cv: 1.2024191406698308
Log Loss train: 1.1001196279920487
for n_estimators = 2000 and max depth = 5
Log Loss cv: 1.1083923280407713
Log Loss train: 0.8684530548880646
for n_estimators = 2000 and max depth = 7
Log Loss cv: 1.0670849255877433
Log Loss train: 0.687383236130885
for n_estimators = 2000 and max depth = 10
Log Loss cv: 1.049339910790633
Log Loss train: 0.518731457270126
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
(2124, 1018) (2124,)
(532, 1018) (532,)
(665, 1018) (665,)
Log Loss cv: 1.12511787673414
Log Loss train: 0.8844410482107985
Log Loss test: 1.1406196373627946
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning:
invalid value encountered in true_divide
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
4.6 Lets apply Stacking classifier</p>
</div>
</div>
</div>
Applying Stacking Classifer on MeanResponse Coding
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)
Log loss (train) on the stacking classifier : 0.14030289465971651
Log loss (cv) on the stacking classifier : 0.2420519229697048
Log loss (train) on the stacking classifier : 0.25972844103712256
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
(5390, 1018) (5390,)
(1348, 1018) (1348,)
(1685, 1018) (1685,)
for meta classifier i can take my c values as 0.1
Applyin stacking for onehotfeatures
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-1-4ee702f64944> in <module>()
----> 1 stackingClassifier_tunning(xres,yres)
NameError: name 'stackingClassifier_tunning' is not defined
taking c as 1
stackingClassifier(xres,yres)
(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.30903590626810395
Log loss (cv) on the stacking classifier : 0.5317070749588001
Log loss (train) on the stacking classifier : 0.5271830152079766
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
taking c as 0.1
stackingClassifier(xres,yres)
(5376, 4260) (5376,)
(1344, 4260) (1344,)
(1681, 4260) (1681,)
Log loss (train) on the stacking classifier : 0.31039276656264053
Log loss (cv) on the stacking classifier : 0.5340512149631327
Log loss (train) on the stacking classifier : 0.5269476489328632
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
Applyin stacking for hashed Featurisation
std_gene_var_text_hashingtfidf
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1
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4
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6
7
8
aberrant
ability
abl
able
absence
according
acid
acids
acquired
across
activate
activated
activating
activating mutations
activation
activation loop
active
activities
activity
added
addition
additional
...
university
unknown
upon
use
used
using
value
values
variant
variants
various
vector
version
versus
vhl
via
view
vitro
vivo
volume
vus
vus neutral
washed
weeks
well
western
western blot
whereas
whether
whole
wild
wild type
will
within
without
wt
years
yeast
yet
Class
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4
3321 rows × 1019 columns
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning:
Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24.
stackingClassifier_tunning(xres,yres)
(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
(5358, 1018) (5358,)
(1340, 1018) (1340,)
(1675, 1018) (1675,)
Log loss (train) on the stacking classifier : 0.31306871841068806
Log loss (cv) on the stacking classifier : 0.5266414657786868
Log loss (train) on the stacking classifier : 0.5249250625775747
-------------------- Confusion matrix --------------------
-------------------- Recall matrix (Row sum=1) --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
5.0 Final Observations Preetytableformat</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
| Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
| Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 |
| Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 |
| gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 |
| gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 |
| gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 |
| gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 |
| gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 |
| gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 |
| gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 |
| gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 |
| gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 |
| gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 |
| gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 |
| gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 |
| gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 |
| gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 |
| gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 |
| gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 |
| gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 |
| gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 |
| gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 |
+---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
- 4.1 Creating Miscellenous Functions
- Confusion Matrix,Precision,Recall</p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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-0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 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-0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 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0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 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0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 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0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> def plot_confusion_matrix(test_y, predict_y): C = confusion_matrix(test_y, predict_y) # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j A =(((C.T)/(C.sum(axis=1))).T) #divid each element of the confusion matrix with the sum of elements in that column # C = [[1, 2], # [3, 4]] # C.T = [[1, 3], # [2, 4]] # C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =1) = [[3, 7]] # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7] # [2/3, 4/7]] # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3] # [3/7, 4/7]] # sum of row elements = 1 B =(C/C.sum(axis=0)) #divid each element of the confusion matrix with the sum of elements in that row # C = [[1, 2], # [3, 4]] # C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array # C.sum(axix =0) = [[4, 6]] # (C/C.sum(axis=0)) = [[1/4, 2/6], # [3/4, 4/6]] labels = [1,2,3,4,5,6,7,8,9] # representing A in heatmap format print("-"*20, "Confusion matrix", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() # representing B in heatmap format print("-"*20, "Recall matrix (Row sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20) plt.figure(figsize=(20,7)) sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels) plt.xlabel('Predicted Class') plt.ylabel('Original Class') plt.show() Loading the df and manupulating #Gene DF gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv") gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv") gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv") #Variation DF variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv") variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv") variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv") #Text DF text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv") text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv") #originalDF df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv") variation_df_featureHashing.columns Index(['Unnamed: 0', '0', '1', '2', '3', '4', '5', '6', '7', '8', 'Class'], dtype='object') gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class']) gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class']) gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class']) variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class']) variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class']) variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class']) text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class']) text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class']) df=df.drop(columns=['Unnamed: 0']) gene_vector_meanResponse.columns variation_df_meanResponse.columns text_df_tfidf.columns Index(['aberrant', 'ability', 'abl', 'able', 'absence', 'according', 'acid', 'acids', 'acquired', 'across', ... 'whole', 'wild', 'wild type', 'will', 'within', 'without', 'wt', 'years', 'yeast', 'yet'], dtype='object', length=1000) #1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version #2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version #3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version #4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised #5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version #6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version #and many more combinations can be done,, we will focus mailny on tfidf version and mean response ##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors ### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures. 1.(gene meanResponse + variation meanResponse + text tfidf) standardised version dataset gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1) withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1) gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns strndzn=StandardScaler() std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf) std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv") 2.(gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised gene_vector_oneHot,variation_df_oneHot,text_df_tfidf clmns=text_df_tfidf.columns text_stdr=strndzn.fit_transform(text_df_tfidf) df1=pd.DataFrame(text_stdr,columns=clmns) std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1) without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1) std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv') std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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-0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1) std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv') std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 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-0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns Now we have our data sets lets perform modelling. Datasets info:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities. Lets Create Models 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 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0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 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0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 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0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- 3.gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords) Standardised version
- 4.1 NaiveBayes Model</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 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FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 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-0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A. gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_NB=without_std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_NB) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) def naiveBayesTunning(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000] for i in Alpha: MNB = MultinomialNB(alpha=i) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(np.log(Alpha), train_logloss, label='Train Logloss') plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss') plt.legend() plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss') plt.xlabel("log(alpha)") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) naiveBayesTunning(varA_NB,varB_NB) From the above graph i can take my alpha value as 1. def naiveBayesTesting_(var1,var2): """ This function is used to tune naiveBayes Model on data and find right Hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] MNB = MultinomialNB(alpha=1) MNB.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(MNB, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss)) print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) naiveBayesTesting_(varA_NB,varB_NB) The hyper parameter and logloss for Train data are :1 and [0.9633295236937504] The hyper parameter and logloss for Train data are :1 and [1.1924015413394058] The hyper parameter and logloss for Train data are :1 and [1.1982778811800479] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- This is the Base Line Model Results it performed well. B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) withoutstd_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.540984 0.060109 0.054645 0.060109 0.054645 0.065574 0.054645 0.054645 0.054645 0.007393 0.002483 0.000000 0.000000 0.007285 0.004777 0.010791 0.002667 0.000000 0.000000 0.003147 0.002779 0.005502 0.00000 0.002177 0.0 0.005421 0.000000 0.021727 0.025389 0.000000 0.006328 ... 0.000000 0.011351 0.011977 0.000000 0.009670 0.024498 0.000000 0.000000 0.000000 0.000000 0.002678 0.007361 0.000000 0.000000 0.0 0.000000 0.000000 0.015961 0.000000 0.003635 0.0 0.0 0.014516 0.000000 0.005902 0.047440 0.048425 0.004343 0.010854 0.000000 0.006067 0.006067 0.004734 0.006265 0.007224 0.028735 0.000000 0.017848 0.005846 1 1 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 2 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.007145 0.000000 0.000000 0.005449 0.001641 0.000000 0.002820 0.002611 0.002394 0.002114 0.004184 0.00000 0.004967 0.0 0.000000 0.002438 0.021031 0.000000 0.009145 0.003209 ... 0.007657 0.002158 0.002277 0.000000 0.007354 0.034397 0.000000 0.000000 0.000000 0.000000 0.006110 0.013063 0.000000 0.000000 0.0 0.002291 0.000000 0.000000 0.000000 0.002765 0.0 0.0 0.022081 0.000000 0.008978 0.004245 0.005261 0.001651 0.003302 0.004868 0.010767 0.010767 0.003600 0.001588 0.005494 0.013112 0.002496 0.000000 0.006669 2 3 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.008309 0.002790 0.015838 0.006590 0.010917 0.002684 0.002426 0.005995 0.058335 0.003859 0.000000 0.006248 0.009276 0.00846 0.004893 0.0 0.000000 0.003603 0.033300 0.003567 0.002252 0.007113 ... 0.000000 0.012758 0.003365 0.000000 0.006521 0.027534 0.000000 0.003180 0.016809 0.025671 0.000000 0.005516 0.003996 0.000000 0.0 0.003385 0.018474 0.000000 0.000000 0.004085 0.0 0.0 0.008158 0.000000 0.013268 0.000000 0.000000 0.009762 0.017079 0.007194 0.036367 0.036367 0.002660 0.004694 0.013531 0.009689 0.007378 0.000000 0.000000 3 4 0.104348 0.104348 0.095652 0.226087 0.113043 0.095652 0.086957 0.086957 0.086957 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.003513 0.000000 0.000000 0.000000 0.020280 0.003054 0.001887 0.000000 0.000000 0.002227 0.007867 0.000000 0.00000 0.024644 0.0 0.026850 0.000000 0.048918 0.002246 0.001418 0.004478 ... 0.000000 0.006024 0.008475 0.003597 0.010948 0.018668 0.017570 0.012011 0.000000 0.004040 0.003789 0.006945 0.007547 0.000000 0.0 0.004263 0.000000 0.000000 0.001934 0.000000 0.0 0.0 0.005136 0.000000 0.001392 0.000000 0.000000 0.006146 0.001536 0.009058 0.028620 0.028620 0.001675 0.005911 0.003408 0.000000 0.002323 0.000000 0.002068 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006259 0.000000 0.003696 0.001531 0.016559 0.005441 0.000000 0.007010 0.000000 0.000000 0.000000 0.000000 0.00000 0.006860 0.0 0.000000 0.000000 0.004980 0.006001 0.010105 0.001330 ... 0.008461 0.005366 0.000000 0.000000 0.013408 0.039196 0.000000 0.001783 0.000000 0.000000 0.003375 0.012373 0.000000 0.002139 0.0 0.003797 0.002072 0.000000 0.008612 0.006873 0.0 0.0 0.004575 0.005105 0.004960 0.012312 0.010901 0.000000 0.005473 0.000000 0.008923 0.008923 0.007459 0.007898 0.009106 0.048900 0.000000 0.000000 0.000000 4 3317 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.006038 0.000000 0.004754 0.001969 0.009682 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.009749 0.001710 ... 0.005442 0.006902 0.000000 0.000000 0.014111 0.030556 0.000000 0.000000 0.000000 0.000000 0.004342 0.009947 0.000000 0.002751 0.0 0.004884 0.002665 0.000000 0.011078 0.002947 0.0 0.0 0.002942 0.006567 0.001595 0.009050 0.008413 0.000000 0.007040 0.000000 0.001640 0.001640 0.009594 0.005079 0.009761 0.062901 0.000000 0.000000 0.000000 1 3318 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.096774 0.330645 0.080645 0.080645 0.080645 0.080645 0.080645 0.088710 0.080645 0.000000 0.001136 0.000000 0.000000 0.000000 0.001093 0.003950 0.009763 0.000000 0.000000 0.000000 0.001272 0.000000 0.00000 0.011951 0.0 0.000000 0.000000 0.002711 0.000000 0.001834 0.000000 ... 0.001535 0.003895 0.002740 0.000000 0.000000 0.006035 0.000000 0.000000 0.005474 0.002613 0.002450 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.001800 0.000000 0.000000 0.001987 0.001986 0.000000 0.005552 0.005552 0.005414 0.001911 0.003305 0.000000 0.001502 0.000000 0.001337 1 3319 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.000000 0.000000 0.000000 0.000000 0.001889 0.005121 0.004219 0.017595 0.000000 0.000000 0.000000 0.000000 0.00000 0.000000 0.0 0.004288 0.012680 0.012499 0.000000 0.012681 0.003337 ... 0.000000 0.002245 0.000000 0.000000 0.003059 0.000000 0.000000 0.000000 0.000000 0.000000 0.004236 0.000000 0.000000 0.000000 0.0 0.002383 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.007781 0.000000 0.000000 0.006870 0.000000 0.005063 0.007998 0.007998 0.007488 0.003304 0.001905 0.002273 0.007789 0.000000 0.006936 4 3320 0.131579 0.087719 0.087719 0.228070 0.087719 0.096491 0.105263 0.087719 0.087719 0.109890 0.109890 0.109890 0.120879 0.109890 0.109890 0.109890 0.109890 0.109890 0.000000 0.002173 0.000000 0.000000 0.002125 0.002090 0.005667 0.010505 0.012980 0.000000 0.000000 0.000000 0.000000 0.00000 0.001905 0.0 0.002372 0.016837 0.017288 0.000000 0.009648 0.006462 ... 0.000000 0.002484 0.002621 0.003337 0.009310 0.009072 0.001358 0.000000 0.000000 0.000000 0.004687 0.003222 0.000000 0.000000 0.0 0.002636 0.000000 0.001996 0.002392 0.000000 0.0 0.0 0.003176 0.000000 0.006027 0.000000 0.000000 0.006652 0.000000 0.002801 0.023896 0.023896 0.005179 0.010053 0.004215 0.002515 0.004310 0.000000 0.003837 4 3321 rows × 1019 columns varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values varB_NB_=without_std_gene_var_text_onehottifidf.Class.values Reusing the above functions naiveBayesTunning(varA_NB_ , varB_NB_) from above graph i can select alpha as 1 naiveBayesTesting_(varA_NB_ , varB_NB_) The hyper parameter and logloss for Train data are :1 and [1.0383832549539975] The hyper parameter and logloss for Train data are :1 and [1.2612556656065605] The hyper parameter and logloss for Train data are :1 and [1.2320081611414992] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.2 Knn Model</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) dash_dfff=std_gene_var_text_onehottifidf varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_knn=std_gene_var_text_onehottifidf.Class.values from collections import Counter Counter(varB_knn) Counter({1: 568, 2: 452, 3: 89, 4: 686, 5: 242, 6: 275, 7: 953, 8: 19, 9: 37}) Balancing the data set is important in knn as knn doesnt have balancing option in model parameters from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(varA_Knn,varB_knn) Counter(y_res) Counter({1: 917, 2: 921, 3: 945, 4: 926, 5: 932, 6: 941, 7: 913, 8: 953, 9: 953}) def knnTuning(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81] for i in K: neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) #y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) #test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) plt.plot(K, train_logloss, label='Train Logloss') plt.plot(K, cv_logloss ,label='CV logloss') plt.legend() plt.scatter(K,cv_logloss , label='CV logloss') plt.xlabel("K") plt.ylabel("logloss") plt.title("ERROR PLOTS") return(plt.show()) knnTuning(x_res,y_res) From above graph i can select my k value as 31 def knnTesting(var1, var2): """ This Function is used to perform KNNtuning and decide the K hyper parameter """ X=var1 Y=var2 #Splitting the data x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2) #hyperTuning train_logloss = [] cv_logloss = [] test_logloss = [] neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf.fit(x_train,y_train) y_train_pred = sig_clf.predict_proba(x_train) y_cv_pred = sig_clf.predict_proba(x_cv) y_test_pred = sig_clf.predict_proba(x_test) train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15)) cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15)) test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15)) print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss)) print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss)) print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss)) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.813976747977174] The hyper parameter and logloss for cv data are :31 and [0.8788933809062333] The hyper parameter and logloss for Test data are :31 and [0.8810438075247502] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values y_mean=std_gene_var_text_meanTdidf.Class.values balancing class using SMOTE from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_mean,y_mean) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 921, 2: 926, 3: 949, 4: 926, 5: 932, 6: 944, 7: 919, 8: 953, 9: 953}) Tuning using the previous functions knnTuning(x_res,y_res) from the above graph i can take k =21 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :31 and [0.6814415244209885] The hyper parameter and logloss for cv data are :31 and [0.7016829214824364] The hyper parameter and logloss for Test data are :31 and [0.7779077172643827] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation (hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 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-0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values y_hashed=std_gene_var_text_hashingtfidf.Class.values Balancing using smote from imblearn.combine import SMOTETomek smk=SMOTETomek(random_state=42) x_res,y_res=smk.fit_sample(x_hashed,y_hashed) from collections import Counter Counter(y_res) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Counter({1: 915, 2: 916, 3: 944, 4: 923, 5: 924, 6: 938, 7: 907, 8: 953, 9: 953}) tuning model and testing model with old functions created above knnTuning(x_res,y_res) From the graph i can take k as 15 knnTesting(x_res,y_res) The hyper parameter and logloss for Train data are :15 and [0.6474458212209986] The hyper parameter and logloss for cv data are :15 and [0.7512635680512498] The hyper parameter and logloss for Test data are :31 and [0.7368556387957963] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 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-0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- 4.2 Knn Model</p>
- </p>
- 4.3 Logistic Regression</p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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-0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 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-1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 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-0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 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0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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-0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 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-0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varB_log=std_gene_var_text_onehottifidf.Class.values def tune_Logsitic(var1,var2): """ This function is use to build n hyperparamater tune logisticmodel """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tune_Logsitic(varA_log,varB_knn) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) From the graph i can take aplha as 1 def test_Logistic(var1,var2): """ This function is used to test model on test data. """ x_true=var1 y_true=var2 x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2) print("The shape of the train n test vector as follows:") print(x_train.shape,y_train.shape) print(x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) log_loss_train=[] log_loss_cv=[] log_loss_test=[] model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' ) model.fit(x_train , y_train) clf=CalibratedClassifierCV(model,method='sigmoid',cv=5) clf.fit(x_train,y_train) pred_ytrain=clf.predict_proba(x_train) log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15)) pred_ycv=clf.predict_proba(x_cv) log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15)) pred_ytest=clf.predict_proba(x_test) log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15)) print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train )) print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv )) print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test )) plot_confusion_matrix(y_test, clf.predict(x_test)) test_Logistic(varA_log,varB_log) The shape of the train n test vector as follows: (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logloss for 1 the coresponding train loss is [0.7850156568089277] The Logloss for 1 the coresponding cv loss is [1.1961412442201744] The Logloss for 1 the coresponding test loss is [1.1748189424714095] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 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-0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean=std_gene_var_text_meanTdidf.Class.values for tunning and testing using above functions tune_Logsitic(vara_mean,varb_mean) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from the above graph i can conclude c=1 test_Logistic(vara_mean,varb_mean) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.5847329559769139] The Logloss for 1 the coresponding cv loss is [0.8572068041315724] The Logloss for 1 the coresponding test loss is [0.8214264881831812] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 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... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 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0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_hashed=std_gene_var_text_hashingtfidf.Class.values tune_Logsitic(vara_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from graph i can take C as 1 test_Logistic(vara_hashed,varb_hashed) The shape of the train n test vector as follows: (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logloss for 1 the coresponding train loss is [0.8735667598121121] The Logloss for 1 the coresponding cv loss is [1.1928582990002885] The Logloss for 1 the coresponding test loss is [1.1999154339757783] -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.4 SVM </p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 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-0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 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-0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_svm=std_gene_var_text_onehottifidf.Class.values def tuneSVM(var1,var2): """ This function is use to build n hyperparamater tune SVM """ X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) #Svm HyperTuning logLoss_train=[ ] logLoss_cv=[ ] c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ] for i in c: clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced') clf_SGD.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y = calibrated.predict_proba(x_train) predict_y2 = calibrated.predict_proba(x_cv) logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15)) logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15)) plt.plot(np.log(c), logLoss_train, label='Train logloss') plt.plot(np.log(c), logLoss_cv, label='CV logloss') plt.scatter(np.log(c), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C: hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) tuneSVM(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) from the graph i can select c = 0.1 def test_SVMModel(var1 , var2): X = var1 y_true = var2 x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) logLoss_test=[ ] logLoss_cv_new=[ ] logLoss_train_new=[ ] clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced') clf_SGD_test.fit(x_train,y_train) calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5) calibrated.fit(x_train , y_train) predict_y_test = calibrated.predict_proba(x_test) logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15)) predicted_y_train=calibrated.predict_proba(x_train) logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15)) predicted_y_cv=calibrated.predict_proba(x_cv) logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15)) print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new )) print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new )) print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test)) plot_confusion_matrix(y_test, calibrated.predict(x_test)) test_SVMModel(vara_svm,varb_svm) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) The Logg loss for training data with best aplha 0.1 is [0.9361514695402671] The Logg loss for cv data with best aplha 0.1 is [1.1565827483656932] The Logg loss for test data with best aplha 0.1 is [1.1689839246304874] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 1.239260 4.962103 -1.892613 -3.778908 -3.522230 -3.725716 -3.973595 -1.866393 -3.850622 -3.799530 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 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-0.280409 0.835527 4 3320 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values varb_mean_svm=std_gene_var_text_meanTdidf.Class.values using previous functions for model tuneSVM(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above c =1 test_SVMModel(vara_mean_svm,varb_mean_svm) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.8782130606666145] The Logg loss for cv data with best aplha 1 is [1.104589994061247] The Logg loss for test data with best aplha 1 is [1.0574913033666131] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 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-0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values tuneSVM(vara_svm_hashed,varb_svm_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) from above graph i can take c as 1 test_SVMModel(vara_svm_hashed,varb_hashed) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) The Logg loss for training data with best aplha 1 is [0.9725367454691902] The Logg loss for cv data with best aplha 1 is [1.1331135752614927] The Logg loss for test data with best aplha 1 is [1.2037472465904475] -------------------- Confusion matrix -------------------- /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.5 Random Forest</p> DatasetsUsedHere:- gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) gene(hashed) + variation(hashed) + text(tfidf-Bigrams) </div> </div> </div> A.gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams) std_gene_var_text_onehottifidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } ABL1 ACVR1 AGO2 AKT1 AKT2 AKT3 ALK APC AR ARAF ARID1A ARID1B ARID2 ARID5B ASXL1 ASXL2 ATM ATR ATRX AURKA AURKB AXIN1 AXL B2M BAP1 BARD1 BCL10 BCL2 BCL2L11 BCOR BRAF BRCA1 BRCA2 BRD4 BRIP1 BTK CARD11 CARM1 CASP8 CBL ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 4261 columns vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values def tune_randomforest(vara,varb): """ This function is used to tune rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] estimators = [100,200,500,1000,2000] max_depth = [3, 5, 7,10] for i in estimators: for j in max_depth: print("for n_estimators =", i,"and max depth = ", j) clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) tune_randomforest(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2606812233244562 Log Loss train: 1.1374431015135666 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1653337133462423 Log Loss train: 0.9371070459846149 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.12209548509024 Log Loss train: 0.7363291411007856 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.10268480641603 Log Loss train: 0.5519688514792611 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.248631824458489 Log Loss train: 1.1184306206599595 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1564275473529837 Log Loss train: 0.919144190096995 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.110794380130624 Log Loss train: 0.7207481407448507 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0961422234706304 Log Loss train: 0.5423272076360501 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.2478678013942157 Log Loss train: 1.114370022235754 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.148009893446 Log Loss train: 0.9050771353940641 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.1026865237241201 Log Loss train: 0.7101699614090803 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0917480510399207 Log Loss train: 0.5373319633612905 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.2414453238075647 Log Loss train: 1.1053160288591364 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1452379981760221 Log Loss train: 0.900926228080795 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0990520521977565 Log Loss train: 0.7050443392267894 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0908700848502797 Log Loss train: 0.5357777692154363 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.240682648359384 Log Loss train: 1.1036497793577018 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1411916861993632 Log Loss train: 0.8950347894321122 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0971090158549015 Log Loss train: 0.7018660431272183 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.0925882642141658 Log Loss train: 0.5355792048858152 from above data i can select esimators 2000 and depth=5 def testRF(vara,varb): """ This function is used to test rf model """ X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) # HyperTuning logLoss_train=[ ] logLoss_cv=[ ] logLoss_test=[ ] clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1) clf.fit(x_train, y_train) sig_clf = CalibratedClassifierCV(clf, method="sigmoid") sig_clf.fit(x_train, y_train) sig_clf_probs_ytrain = sig_clf.predict_proba(x_train) sig_clf_probs_ycv = sig_clf.predict_proba(x_cv) sig_clf_probs_ytest = sig_clf.predict_proba(x_test) logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15)) logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15)) logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15)) print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv)) print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain )) print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest )) plot_confusion_matrix(y_test, sig_clf.predict(x_test)) testRF(vara_RF_onhot,varb_RF_onhot) (2124, 4260) (2124,) (532, 4260) (532,) (665, 4260) (665,) Log Loss cv: 1.11768909971263 Log Loss train: 0.9002694349418067 Log Loss test: 1.1477524391499037 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams) std_gene_var_text_meanTdidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 -0.051316 -0.079307 1.060311 -0.313257 0.019198 -0.009538 -0.465647 1.394926 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-0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 0.290503 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 1.077846 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 -0.280827 -0.167630 0.464136 0.727786 0.075276 -0.203730 -0.672016 0.484531 0.416477 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.227740 -0.104270 0.272057 0.728993 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.095446 -0.104270 0.272057 -0.039756 0.228389 0.126561 -0.144169 0.289721 0.287701 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 0.097234 -0.432626 0.131969 0.745554 -0.375078 -0.192286 -0.507283 0.514811 0.443843 -0.385640 7.826208 -1.872384 -2.085619 -1.864777 -2.579187 -1.055855 -1.297640 -1.875937 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 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-0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018] varb_mean_rf=std_gene_var_text_meanTdidf.Class.values tune_randomforest(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 0.23570046546599333 Log Loss train: 0.19363398591104833 for n_estimators = 100 and max depth = 5 Log Loss cv: 0.22955561081586118 Log Loss train: 0.16166542392105412 for n_estimators = 100 and max depth = 7 Log Loss cv: 0.23627364969006484 Log Loss train: 0.13622754279491436 for n_estimators = 100 and max depth = 10 Log Loss cv: 0.23299169854143326 Log Loss train: 0.09219438260645411 for n_estimators = 200 and max depth = 3 Log Loss cv: 0.15347165131328178 Log Loss train: 0.1289817833963096 for n_estimators = 200 and max depth = 5 Log Loss cv: 0.18867692478728829 Log Loss train: 0.1333800470656138 for n_estimators = 200 and max depth = 7 Log Loss cv: 0.19812746725522537 Log Loss train: 0.11363125813131297 for n_estimators = 200 and max depth = 10 Log Loss cv: 0.2073843284191654 Log Loss train: 0.08264026371353654 for n_estimators = 500 and max depth = 3 Log Loss cv: 0.14838448934316534 Log Loss train: 0.12796172100789224 for n_estimators = 500 and max depth = 5 Log Loss cv: 0.1732893968732289 Log Loss train: 0.1257528101073332 for n_estimators = 500 and max depth = 7 Log Loss cv: 0.18502788808193749 Log Loss train: 0.1059774981029388 for n_estimators = 500 and max depth = 10 Log Loss cv: 0.20316074184161176 Log Loss train: 0.0820099522892585 for n_estimators = 1000 and max depth = 3 Log Loss cv: 0.15096173695782245 Log Loss train: 0.13021826710995227 for n_estimators = 1000 and max depth = 5 Log Loss cv: 0.17583691544250576 Log Loss train: 0.12659716596839768 for n_estimators = 1000 and max depth = 7 Log Loss cv: 0.1862603932648018 Log Loss train: 0.10603952186939303 for n_estimators = 1000 and max depth = 10 Log Loss cv: 0.20453842968817895 Log Loss train: 0.08168228699376241 for n_estimators = 2000 and max depth = 3 Log Loss cv: 0.14749154103551826 Log Loss train: 0.1272450725093758 for n_estimators = 2000 and max depth = 5 Log Loss cv: 0.16918496782525078 Log Loss train: 0.12115201697127558 for n_estimators = 2000 and max depth = 7 Log Loss cv: 0.1820690821751895 Log Loss train: 0.10282294620866399 for n_estimators = 2000 and max depth = 10 Log Loss cv: 0.1984320940418906 Log Loss train: 0.07940530139460128 from the above i can take estimators as 2000 and depth as 3 testRF(vara_mean_rf,varb_mean_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 0.16118368189224744 Log Loss train: 0.1295171475728342 Log Loss test: 0.17283674779043046 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- C.gene(hashed) + variation(hashed) + text(tfidf-Bigrams) std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 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varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values tune_randomforest(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) for n_estimators = 100 and max depth = 3 Log Loss cv: 1.2562525882383961 Log Loss train: 1.1495093667153717 for n_estimators = 100 and max depth = 5 Log Loss cv: 1.1414141205937414 Log Loss train: 0.9137821932641781 for n_estimators = 100 and max depth = 7 Log Loss cv: 1.0891671228800952 Log Loss train: 0.7274585518989484 for n_estimators = 100 and max depth = 10 Log Loss cv: 1.0602735362314917 Log Loss train: 0.5419761320891583 for n_estimators = 200 and max depth = 3 Log Loss cv: 1.2256031707183694 Log Loss train: 1.1125457127985048 for n_estimators = 200 and max depth = 5 Log Loss cv: 1.1239819425283584 Log Loss train: 0.8863029989503483 for n_estimators = 200 and max depth = 7 Log Loss cv: 1.0779197694234626 Log Loss train: 0.7025060768309693 for n_estimators = 200 and max depth = 10 Log Loss cv: 1.0544180795539924 Log Loss train: 0.5270883620171445 for n_estimators = 500 and max depth = 3 Log Loss cv: 1.207676966917158 Log Loss train: 1.1036352816983548 for n_estimators = 500 and max depth = 5 Log Loss cv: 1.1147682551650802 Log Loss train: 0.8732109339573471 for n_estimators = 500 and max depth = 7 Log Loss cv: 1.0736966943880142 Log Loss train: 0.6951080822600649 for n_estimators = 500 and max depth = 10 Log Loss cv: 1.0509723046279644 Log Loss train: 0.522813716084056 for n_estimators = 1000 and max depth = 3 Log Loss cv: 1.203560840318964 Log Loss train: 1.100936649316696 for n_estimators = 1000 and max depth = 5 Log Loss cv: 1.1104963322013828 Log Loss train: 0.8689259823733123 for n_estimators = 1000 and max depth = 7 Log Loss cv: 1.0701797377723286 Log Loss train: 0.6895920716141755 for n_estimators = 1000 and max depth = 10 Log Loss cv: 1.0502619913750308 Log Loss train: 0.5205027839317523 for n_estimators = 2000 and max depth = 3 Log Loss cv: 1.2024191406698308 Log Loss train: 1.1001196279920487 for n_estimators = 2000 and max depth = 5 Log Loss cv: 1.1083923280407713 Log Loss train: 0.8684530548880646 for n_estimators = 2000 and max depth = 7 Log Loss cv: 1.0670849255877433 Log Loss train: 0.687383236130885 for n_estimators = 2000 and max depth = 10 Log Loss cv: 1.049339910790633 Log Loss train: 0.518731457270126 from looking above i can take estimators as 1000 and depth as 5 testRF(vara_hashed_rf,varb_hashed_rf) (2124, 1018) (2124,) (532, 1018) (532,) (665, 1018) (665,) Log Loss cv: 1.12511787673414 Log Loss train: 0.8844410482107985 Log Loss test: 1.1406196373627946 /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:21: RuntimeWarning: invalid value encountered in true_divide -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- 4.4 SVM </p>
- </p>
- 4.5 Random Forest</p>
- </p>
- B.gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
- 4.6 Lets apply Stacking classifier</p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> Applying Stacking Classifer on MeanResponse Coding X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values Y=std_gene_var_text_meanTdidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(X,Y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. Models def stackingClassifier(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15) print("Log loss (cv) on the stacking classifier :",log_error) log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15) print("Log loss (train) on the stacking classifier :",log_error) plot_confusion_matrix(y_test , sF.predict(x_test)) stackingClassifier(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) Log loss (train) on the stacking classifier : 0.14030289465971651 Log loss (cv) on the stacking classifier : 0.2420519229697048 Log loss (train) on the stacking classifier : 0.25972844103712256 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- def stackingClassifier_tunning(vara,varb): X = vara y_true = varb x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2) x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2) print ( x_train.shape,y_train.shape) print( x_cv.shape,y_cv.shape) print(x_test.shape,y_test.shape) hp=[0.0001,0.001,0.01,0.1,1,10,15] logLoss_train=[] logLoss_test=[] logLoss_cv=[] for i in hp: neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1) neigh.fit(x_train, y_train) sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid") sig_clf0.fit(x_train,y_train) model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' ) model.fit(x_train , y_train) sig_clf1 = CalibratedClassifierCV(model, method="sigmoid") sig_clf1.fit(x_train,y_train) clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1) clf_SGD_test.fit(x_train,y_train) sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid") sig_clf2.fit(x_train,y_train) meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000) sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True) sF.fit(x_train, y_train) logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)) logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)) #logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)) plt.plot(np.log(hp), logLoss_train, label='Train logloss') plt.plot(np.log(hp), logLoss_cv, label='CV logloss') plt.scatter(np.log(hp), logLoss_cv, label='CV logloss') plt.legend() plt.xlabel("C:hyperparameter") plt.ylabel("LogLoss") plt.title("ERROR PLOTS") return(plt.show()) stackingClassifier_tunning(xres,yres) (5390, 1018) (5390,) (1348, 1018) (1348,) (1685, 1018) (1685,) for meta classifier i can take my c values as 0.1 Applyin stacking for onehotfeatures x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values y=std_gene_var_text_onehottifidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. reusing above functions im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters. stackingClassifier_tunning(xres,yres) --------------------------------------------------------------------------- NameError Traceback (most recent call last) <ipython-input-1-4ee702f64944> in <module>() ----> 1 stackingClassifier_tunning(xres,yres) NameError: name 'stackingClassifier_tunning' is not defined taking c as 1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.30903590626810395 Log loss (cv) on the stacking classifier : 0.5317070749588001 Log loss (train) on the stacking classifier : 0.5271830152079766 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- taking c as 0.1 stackingClassifier(xres,yres) (5376, 4260) (5376,) (1344, 4260) (1344,) (1681, 4260) (1681,) Log loss (train) on the stacking classifier : 0.31039276656264053 Log loss (cv) on the stacking classifier : 0.5340512149631327 Log loss (train) on the stacking classifier : 0.5269476489328632 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- Applyin stacking for hashed Featurisation std_gene_var_text_hashingtfidf .dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; } 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 aberrant ability abl able absence according acid acids acquired across activate activated activating activating mutations activation activation loop active activities activity added addition additional ... university unknown upon use used using value values variant variants various vector version versus vhl via view vitro vivo volume vus vus neutral washed weeks well western western blot whereas whether whole wild wild type will within without wt years yeast yet Class 0 0.0 2.0 0.0 0.0 1.0 0.0 0.0 0.0 -1.0 -1.0 -1.0 0.0 -8.0 5.0 0.0 0.0 0.0 1.0 0.400946 -0.430196 -0.184019 -0.598562 0.106869 -0.212540 -0.022161 -0.337083 -0.330649 -0.409547 -0.187608 -0.424438 -0.226977 -0.418689 -0.750039 -0.223838 -0.255683 -0.477842 -0.372849 3.358930 -1.152002 -0.208619 ... -0.409008 0.967553 0.640615 -0.695099 -0.552055 -0.207312 -0.455797 -0.582681 -0.510787 -0.471206 -0.349121 -0.115402 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 0.396532 -0.607300 -0.011632 -0.245789 -0.15573 1.452333 -0.242315 -0.580938 3.449077 4.815348 -0.565529 0.269847 -0.456641 -0.643767 -0.638359 -0.216982 -0.365107 0.049087 0.173741 -0.344039 0.205134 0.599335 1 1 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 1.0 0.0 0.0 -1.0 0.0 -1.0 0.0 -1.0 1.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 2 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 1.0 1.0 0.0 0.0 -1.0 0.0 0.0 0.0 -0.362082 -0.580778 -0.031967 -0.598562 -0.718283 -0.132538 -0.708116 -0.567283 -0.115777 -0.160787 -0.269431 -0.475207 -0.304266 -0.418689 -0.647424 -0.223838 -0.607166 -0.288020 -0.390787 -0.559165 0.028391 -0.547060 ... 0.358525 -0.246247 -0.244573 -0.695099 -0.687776 0.167715 -0.455797 -0.582681 -0.510787 -0.471206 0.129434 0.279595 -0.453854 -0.416317 -0.109556 -0.245856 -0.571798 -0.883671 -0.607300 -0.087970 -0.245789 -0.15573 2.468120 -0.242315 -0.307651 -0.257400 0.098086 -0.845995 -0.616077 -0.017861 -0.506358 -0.500471 -0.364923 -0.755695 -0.148759 -0.160639 -0.219060 -0.280409 0.777698 2 3 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.495499 -0.411536 0.153029 0.466716 0.518255 -0.461402 -0.649322 -0.049831 4.114808 -0.041934 -0.529313 -0.159873 -0.005597 0.419256 -0.650130 -0.223838 -0.607166 -0.197328 -0.074598 -0.008713 -0.861276 -0.123545 ... -0.409008 1.153278 -0.145281 -0.695099 -0.736630 -0.092305 -0.455797 -0.306719 -0.018791 -0.208746 -0.722514 -0.243253 0.099853 -0.416317 -0.109556 -0.107710 2.166456 -0.883671 -0.607300 0.027874 -0.245789 -0.15573 0.598442 -0.242315 0.073491 -0.621631 -0.476916 -0.000804 1.000054 0.191778 0.242172 0.250663 -0.487615 -0.496293 0.770554 -0.233922 0.025344 -0.280409 -0.667276 3 4 0.0 0.0 -1.0 -1.0 0.0 0.0 -1.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 -1.0 0.0 0.0 -0.362082 -0.367675 -0.184019 -0.598562 -0.718283 1.631348 -0.602193 -0.404395 -0.330649 -0.409547 -0.287524 -0.036362 -0.549747 -0.418689 0.076358 -0.223838 1.133791 -0.477842 0.327926 -0.212612 -0.968967 -0.409365 ... -0.409008 0.264175 0.321014 -0.254817 -0.477152 -0.428174 1.160840 0.459758 -0.510787 -0.429896 -0.194091 -0.144233 0.591953 -0.416317 -0.109556 0.003025 -0.571798 -0.883671 -0.461735 -0.330432 -0.245789 -0.15573 0.192664 -0.242315 -0.981689 -0.621631 -0.476916 -0.377629 -0.823239 0.359822 0.015655 0.023358 -0.616222 -0.394690 -0.387389 -0.441298 -0.227761 -0.280409 -0.219152 4 ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 3316 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 0.0 -2.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.201134 -0.184019 -0.001155 -0.544921 1.188801 -0.423242 -0.567283 0.203568 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.577781 -0.223838 -0.607166 -0.477842 -0.804480 0.366915 0.152312 -0.750915 ... 0.439103 0.177281 -0.452394 -0.695099 -0.332908 0.349518 -0.455797 -0.427921 -0.510787 -0.471206 -0.251820 0.231750 -0.453854 -0.174089 -0.109556 -0.055752 -0.264675 -0.883671 0.041011 0.272381 -0.245789 -0.15573 0.117319 0.045613 -0.664645 0.434850 0.714397 -1.018092 -0.361401 -0.456641 -0.560272 -0.554573 0.138720 -0.228759 0.264392 0.605370 -0.344039 -0.280409 -0.667276 4 3317 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 -0.362082 -0.214524 -0.184019 0.169886 -0.495286 0.370851 -0.831175 -0.567283 -0.330649 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.607166 -0.477842 -0.932825 -0.559165 0.106308 -0.709619 ... 0.136457 0.380127 -0.452394 -0.695099 -0.291700 0.022210 -0.455797 -0.582681 -0.510787 -0.471206 -0.117058 0.063711 -0.453854 -0.104738 -0.109556 0.081459 -0.176745 -0.883671 0.226626 -0.071965 -0.245789 -0.15573 -0.101902 0.128049 -0.963656 0.154916 0.442519 -1.018092 -0.177584 -0.456641 -0.773220 -0.768262 0.417445 -0.464130 0.339301 0.905036 -0.344039 -0.280409 -0.667276 1 3318 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 -2.0 0.0 0.0 -2.0 2.0 0.0 0.0 0.0 -1.0 -0.362082 -0.511881 -0.184019 -0.598562 -0.718283 -0.650696 -0.535052 0.275317 -0.330649 -0.409547 -0.529313 -0.539451 -0.549747 -0.418689 -0.390510 -0.223838 -0.607166 -0.477842 -0.862949 -0.559165 -0.915298 -0.895155 ... -0.255094 -0.016910 -0.202348 -0.695099 -1.118847 -0.906753 -0.455797 -0.582681 -0.350557 -0.444495 -0.380832 -0.625334 -0.453854 -0.416317 -0.109556 -0.534999 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.945418 -0.621631 -0.476916 -0.811028 -0.770403 -0.456641 -0.658833 -0.653477 -0.128113 -0.728743 -0.399116 -0.441298 -0.268853 -0.280409 -0.377515 1 3319 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 1.0 0.0 0.0 -2.0 0.0 0.0 0.0 -1.0 0.0 -0.362082 -0.580778 -0.184019 -0.598562 -0.718283 -0.555976 -0.447225 -0.203114 1.010177 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.830104 -0.223838 -0.329148 0.509249 -0.610693 -0.559165 0.484842 -0.533129 ... -0.409008 -0.234842 -0.452394 -0.695099 -0.939518 -1.135403 -0.455797 -0.582681 -0.510787 -0.471206 -0.131818 -0.625334 -0.453854 -0.416317 -0.109556 -0.234284 -0.571798 -0.883671 -0.607300 -0.330432 -0.245789 -0.15573 -0.497009 -0.242315 -0.414002 -0.621631 -0.476916 -0.302152 -1.003432 -0.000301 -0.587302 -0.581697 0.142572 -0.612429 -0.559303 -0.392649 0.045903 -0.280409 0.835527 4 3320 1.0 -1.0 0.0 0.0 0.0 -1.0 0.0 -1.0 1.0 2.0 0.0 0.0 -1.0 -1.0 0.0 0.0 0.0 0.0 -0.362082 -0.448978 -0.184019 -0.598562 -0.477540 -0.532034 -0.406313 0.339406 0.658483 -0.409547 -0.529313 -0.636457 -0.549747 -0.418689 -0.760025 -0.223838 -0.453345 0.832883 -0.487254 -0.559165 0.093239 -0.194102 ... -0.409008 -0.203261 -0.213225 -0.286641 -0.573143 -0.791726 -0.330814 -0.582681 -0.510787 -0.471206 -0.068877 -0.402170 -0.453854 -0.416317 -0.109556 -0.202242 -0.571798 -0.723597 -0.427242 -0.330432 -0.245789 -0.15573 -0.070460 -0.242315 -0.569848 -0.621631 -0.476916 -0.324893 -1.003432 -0.204158 -0.122463 -0.115241 -0.158855 -0.048728 -0.295031 -0.387465 -0.128293 -0.280409 0.164191 4 3321 rows × 1019 columns x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values y=std_gene_var_text_hashingtfidf.Class.values smk=SMOTETomek(random_state=42) xres,yres=smk.fit_sample(x,y) /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. /usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function safe_indexing is deprecated; safe_indexing is deprecated in version 0.22 and will be removed in version 0.24. stackingClassifier_tunning(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) from above figure i will select c as 0.1 stackingClassifier(xres,yres) (5358, 1018) (5358,) (1340, 1018) (1340,) (1675, 1018) (1675,) Log loss (train) on the stacking classifier : 0.31306871841068806 Log loss (cv) on the stacking classifier : 0.5266414657786868 Log loss (train) on the stacking classifier : 0.5249250625775747 -------------------- Confusion matrix -------------------- -------------------- Recall matrix (Row sum=1) -------------------- -------------------- Precision matrix (Columm Sum=1) -------------------- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- 5.0 Final Observations Preetytableformat</p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
- </p> </div> </div> </div> from prettytable import PrettyTable table=PrettyTable() table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"] table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ]) table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527]) table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259]) table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140]) #print("**************************************************************************************************************************") #bestone table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172]) #print("**************************************************************************************************************************") table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147]) table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057]) table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168]) table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199]) table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821]) table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748]) table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736]) table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777]) table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232]) table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198]) print(table) +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Feature Name | Featurisation Used | Model | HyperParameter | train Logloss | Cv Logloss | test Logloss | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ | Text | TFIDF | Logistic | 0.001 | 0.944 | 1.21 | 1.153 | | Text | BOW | Logistic | 0.001 | 0.906 | 1.544 | 1.214 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | | gene,text,variation | Hashing | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.313 | 0.526 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.31 | 0.534 | 0.526 | | gene,text,variation | OneHotEncoding | StackingClassifier | (1 Metaclassifier-LogisticRegression) | 0.309 | 0.531 | 0.527 | | gene,text,variation | MeanResponseCoding | StackingClassifier | (0.1 Metaclassifier-LogisticRegression) | 0.14 | 0.242 | 0.259 | | gene,text,variation | Hashing | RandomForestClassifier | (estimatores-1000,depth-5) | 0.884 | 1.125 | 1.14 | | gene,text,variation | MeanResponseCoding | RandomForestClassifier | (estimatores-2000,depth-3) | 0.129 | 0.161 | 0.172 | | gene,text,variation | onehotencoding | RandomForestClassifier | (estimatores-2000,depth-5) | 0.9 | 1.117 | 1.147 | | gene,text,variation | Hashing | SVM Classifier | 1 | 0.972 | 1.133 | 1.203 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 1 | 0.878 | 1.104 | 1.057 | | gene,text,variation | MeanResponseCoding | SVM Classifier | 0.1 | 0.936 | 1.151 | 1.168 | | gene,text,variation | Hashed | LogisticRegression | 1 | 0.873 | 1.192 | 1.199 | | gene,text,variation | MeanResponseCoding | LogisticRegression | 1 | 0.584 | 0.857 | 0.821 | | gene,text,variation | onehotencoding | LogisticRegression | 1 | 0.785 | 1.196 | 1.748 | | gene,text,variation | hashed | KNN Clasifier | 15 | 0.647 | 0.751 | 0.736 | | gene,text,variation | MeanResponseCoding | KNN Clasifier | 31 | 0.681 | 0.701 | 0.777 | | gene,text,variation | onehotencoding | KNN Clasifier | 31 | 0.813 | 0.878 | 0.881 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 1.038 | 1.261 | 1.232 | | gene,text,variation | MeanResponseCoding | NB Clasifier | 1 | 0.963 | 1.192 | 1.198 | +---------------------+--------------------+------------------------+-----------------------------------------+----------------+-------------+--------------+ Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set. </div>
1. Business Problem
1.1. Description
Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/
Data: Memorial Sloan Kettering Cancer Center (MSKCC)
Download training_variants.zip and training_text.zip from Kaggle.
Context:
Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/discussion/35336#198462
Problem statement :
Classify the given genetic variations/mutations based on evidence from text-based clinical literature.
1.2. Source/Useful Links
Some articles and reference blogs about the problem statement
1.3. Real-world/Business objectives and constraints.
- No low-latency requirement.
- Interpretability is important.
- Errors can be very costly.
- Probability of a data-point belonging to each class is needed.
2. Machine Learning Problem Formulation
2.1. Data
2.1.1. Data Overview
- Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/data
- We have two data files: one conatins the information about the genetic mutations and the other contains the clinical evidence (text) that human experts/pathologists use to classify the genetic mutations.
- Both these data files are have a common column called ID
-
Data file's information:
- training_variants (ID , Gene, Variations, Class)
- training_text (ID, Text)
2.1.2. Example Data Point
training_variants
ID,Gene,Variation,Class
0,FAM58A,Truncating Mutations,1
1,CBL,W802*,2
2,CBL,Q249E,2
...
training_text
ID,Text
0||Cyclin-dependent kinases (CDKs) regulate a variety of fundamental cellular processes. CDK10 stands out as one of the last orphan CDKs for which no activating cyclin has been identified and no kinase activity revealed. Previous work has shown that CDK10 silencing increases ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2)-driven activation of the MAPK pathway, which confers tamoxifen resistance to breast cancer cells. The precise mechanisms by which CDK10 modulates ETS2 activity, and more generally the functions of CDK10, remain elusive. Here we demonstrate that CDK10 is a cyclin-dependent kinase by identifying cyclin M as an activating cyclin. Cyclin M, an orphan cyclin, is the product of FAM58A, whose mutations cause STAR syndrome, a human developmental anomaly whose features include toe syndactyly, telecanthus, and anogenital and renal malformations. We show that STAR syndrome-associated cyclin M mutants are unable to interact with CDK10. Cyclin M silencing phenocopies CDK10 silencing in increasing c-Raf and in conferring tamoxifen resistance to breast cancer cells. CDK10/cyclin M phosphorylates ETS2 in vitro, and in cells it positively controls ETS2 degradation by the proteasome. ETS2 protein levels are increased in cells derived from a STAR patient, and this increase is attributable to decreased cyclin M levels. Altogether, our results reveal an additional regulatory mechanism for ETS2, which plays key roles in cancer and development. They also shed light on the molecular mechanisms underlying STAR syndrome.Cyclin-dependent kinases (CDKs) play a pivotal role in the control of a number of fundamental cellular processes (1). The human genome contains 21 genes encoding proteins that can be considered as members of the CDK family owing to their sequence similarity with bona fide CDKs, those known to be activated by cyclins (2). Although discovered almost 20 y ago (3, 4), CDK10 remains one of the two CDKs without an identified cyclin partner. This knowledge gap has largely impeded the exploration of its biological functions. CDK10 can act as a positive cell cycle regulator in some cells (5, 6) or as a tumor suppressor in others (7, 8). CDK10 interacts with the ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2) transcription factor and inhibits its transcriptional activity through an unknown mechanism (9). CDK10 knockdown derepresses ETS2, which increases the expression of the c-Raf protein kinase, activates the MAPK pathway, and induces resistance of MCF7 cells to tamoxifen (6). ...
2.2. Mapping the real-world problem to an ML problem
2.2.1. Type of Machine Learning Problem
There are nine different classes a genetic mutation can be classified into => Multi class classification problem
2.2.2. Performance Metric
Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment#evaluation
Metric(s):
- Multi class log-loss
- Confusion matrix
2.2.3. Machine Learing Objectives and Constraints
Objective: Predict the probability of each data-point belonging to each of the nine classes.
Constraints:
- Interpretability
- Class probabilities are needed.
- Penalize the errors in class probabilites => Metric is Log-loss.
- No Latency constraints.
2.3. Train, CV and Test Datasets
Split the dataset randomly into three parts train, cross validation and test with 64%,16%, 20% of data respectively
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly as pt
from scipy import stats
from wordcloud import STOPWORDS
from nltk import SnowballStemmer , PorterStemmer
from bs4 import BeautifulSoup
import re
import collections
import string
import math
from sklearn.preprocessing import LabelEncoder , OneHotEncoder
from sklearn.feature_extraction import FeatureHasher
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.calibration import CalibratedClassifierCV
from sklearn.metrics import log_loss , confusion_matrix
from sklearn.linear_model import LogisticRegression
from prettytable import PrettyTable
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
from sklearn.preprocessing import StandardScaler
from sklearn.naive_bayes import MultinomialNB
from sklearn.neighbors import KNeighborsClassifier
from imblearn.combine import SMOTETomek
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingClassifier
3. Exploratory Data Analysis
df_text=pd.read_csv("/content/drive/My Drive/training_text/training_text" ,sep="\|\|",engine="python",names=["ID","TEXT"],skiprows=1)
df_variants=pd.read_csv("/content/drive/My Drive/training_text/training_variants")
df_text.head()
df_variants.head()
df=pd.merge(df_text,df_variants , on= "ID")
df.head()
df.shape
- There are 3321 Data points and 5 cloumns
df.columns
df.groupby('Class')['ID'].count().plot.bar()
- We can see that the class 1,2,4,7 have failry large amount of data points while 5,6 have less count and 8,9 being very least count of data points . we can see imbalance data set here.
df.info()
df.Gene.describe()
- There are 264 unique genes , and the gene name BRCAI has more frequency of occuring means it is present in that many datapoints and that gene occurs more.
df.Gene.value_counts(ascending=True)
- We can see the most number of repeating genes are BRCAI followed by TP53 ,EGFR and so on...
df.Variation.value_counts()
- The most common type of mutations are Truncating Mutations followed by deletion ,amplification etc.
df.Class.value_counts()
df.head()
Looking for null values
</div> </div> </div>df[df.isna().any(1)]
- A good way to replace them will be by the gene + variation as we cannot drop them or repleace them by empty string due to our objective.
df.loc[df['TEXT'].isnull(),'TEXT'] = df['Gene'] +' '+df['Variation']
df[df.isna().any(1)]
- Before Going further for analysis lets preprocess the text data
def decontracted(phrase):
# specific
phrase = re.sub(r"won't", "will not", phrase)
phrase = re.sub(r"can\'t", "can not", phrase)
# general
phrase = re.sub(r"n\'t", " not", phrase)
phrase = re.sub(r"\'re", " are", phrase)
phrase = re.sub(r"\'s", " is", phrase)
phrase = re.sub(r"\'d", " would", phrase)
phrase = re.sub(r"\'ll", " will", phrase)
phrase = re.sub(r"\'t", " not", phrase)
phrase = re.sub(r"\'ve", " have", phrase)
phrase = re.sub(r"\'m", " am", phrase)
return phrase
def preProccesing( data ):
column_data=data['TEXT']
review_text=[ ]
for sentance in column_data.values:
#1.Removing Urls
sentance=re.sub(r"http\S+" , "" , sentance )
#2.Removing html tags
sentance=re.sub(r"<[^<]+?>", "" , sentance )
#Removing lmxl
soup = BeautifulSoup(sentance, 'lxml')
sentance = soup.get_text()
#3.decontracting phares
sentance=decontracted(sentance)
#4.Removing word with numbers
sentance=re.sub("S*\d\S*" , "" , sentance)
#5.remove Special charactor punc spaces
sentance=re.sub(r"\W+", " ", sentance)
# replace every special char with space
sentance = re.sub('[^a-zA-Z0-9\n]', ' ',sentance)
# replace multiple spaces with single space
sentance = re.sub('\s+',' ', sentance)
sentance = ' '.join(e.lower() for e in sentance.split() if e.lower() not in STOPWORDS)
review_text.append(sentance.strip())
df['Cleaned_text'] = pd.DataFrame(review_text)
Lets Run this function
</div> </div> </div>preProccesing(df)
df.head()
*As we got the cleaned text lets remove the TEXT field from the DataFrame
df=df.drop(columns='TEXT')
df.head()
df[df.isna().any(axis=1)]
df.to_csv("OriginalDFWithCleanedTextPresent.csv")
Original_df=df
df.Class.value_counts()
- lets perform Univariate analysis on these features
3.1 Univariate Analysis
</p>
</div>
</div>
</div>
3.1.2 Analysing Gene Feature
</p>
</div>
</div>
</div>
Q1. what type of feature is gene ?
</div>
</div>
</div>
- It is a Categorical Feature
Q2.How many Categories are there?
</div>
</div>
</div>
df.groupby("Gene")["ID"].count()
- we can see that there are 263 unique Gene categories are present
Q3. Distribution of Gene?
</div>
</div>
</div>
unique_genes = df['Gene'].value_counts()
# the top 10 genes that occured most
print(unique_genes.head(10))
sums=sum(unique_genes.values)
result=unique_genes.values/sums
plt.plot(result,label="Histrogram of Genes")
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.grid()
plt.show()
- There are some genes which occur very less , some genes which occur more
plt.plot(np.cumsum(result),label='Cumulative distribution of Genes')
plt.grid()
plt.legend()
plt.show()
- 80 Percent of the genes have index in range 50-75
Q4 How to featurize this Feature? Gene
</div>
</div>
</div>
1.One-Hot Encoding
2.Mean-ResponseCoding
3.FeatureHashing
</div>
</div>
</div>
- Lets Create data frames to work with this
df_one_hot_encoding=df
df_mean_response_coding=df
df_featureHashing=df
- OneHotEncoding
df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text'])
df_one_hot_encoding.head()
labelencode=LabelEncoder()
df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene'])
df_one_hot_encoding
list_feature_labels=list(labelencode.classes_)
df_one_hot_encoding.head()
onehotencoder=OneHotEncoder()
array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray()
data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels)
data_gen.head()
onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 )
onehotencoded_features_gene.Class.value_counts()
finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables'])
finalOneHotEncodedFeaturesOfGene.Class.value_counts()
- As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene).
finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv")
- Feature Hashing for GeneFeature
We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes.
df_featureHashing.head()
df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text'])
df_featureHashing.head()
hasher=FeatureHasher(n_features=9,input_type='string')
hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray()
dataframe_hashed_features=pd.DataFrame(hased_features)
dataframe_hashed_features.head()
finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1)
finalFeatureHashedFeaturesOfGene.Class.value_counts()
finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv")
- Now Lets go for meanResponseCoding
df_mean_response_coding.head()
df_mean_response_coding.Gene.value_counts()
df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text'])
df_mean_response_coding.Gene.value_counts()
df.head()
len(df)
dummy=df_mean_response_coding[0:3321]
objects=dummy.Gene.value_counts()
def myfunction_one(data):
dummy1=data
my_dicti={}
for i,denominator in objects.items():
vector=[]
for k in range (1,10):
count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)])
vector.append( (count + (1*10)) / (denominator + (1*90)) )
my_dicti[i] = vector
return my_dicti
my_diciti=myfunction_one(dummy)
def vectoresCreation(data):
dummy1=data
eachrow_vector_data=[ ]
for index,row in dummy1.iterrows():
if row["Gene"] in dict(dummy1.Gene.value_counts()).keys():
eachrow_vector_data.append(my_diciti[row["Gene"]])
else :
eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
return(eachrow_vector_data)
eachRow_Vector=vectoresCreation(dummy)
df_meanresponse_vectors=pd.DataFrame(eachRow_Vector)
df_meanresponse_vectors.shape
df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1)
df_meanresponse_vectors.Class.value_counts()
df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv")
Q5.How good is this gene feature in predicting y_i?
We can do this in many ways one is by simple plotting and other by simple models.
- Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well.
finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0')
finalFeatureHashedFeaturesOfGene.head()
finalFeatureHashedFeaturesOfGene.shape
finalFeatureHashedFeaturesOfGene.Class.value_counts()
y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values
X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]]
model to test
def SVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM(X_Hashed,y_true_Hashed)
by seeing the above plot we take alpha as 0.001
def SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test))
SVMModel(X_Hashed,y_true_Hashed)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
lets go to meanresponse coding features and apply SVM
finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0')
finalMeanResponseVectorsOfGene.columns
x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']]
y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values
def SVM_meanResponseCoding(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse)
We ca see that i can take c = 1 from above graph Lets go for testing on test data
def SVM_meanResponseCoding_test(var1,var2):
"""
This function is use to build n test SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 =calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well.
comming to Onehotencodded Features we can use logistic regression due to high dimentions
finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0")
finalOneHotFeatures.columns
x_oneHot=finalOneHotFeatures.iloc[:,0:264 ]
y_true_oneHot=finalOneHotFeatures.Class.values
Model
def LogisticReg_tune(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = []
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ]
for i in c:
clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
LogisticReg_tune(x_oneHot,y_true_oneHot)
- by seeing the above graph i can take log loss as 0.1 .
def LogisticReg_test(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = [ ]
logLoss_test=[ ]
clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 = calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test))
LogisticReg_test(x_oneHot,y_true_oneHot)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well.
- Observations on Gene Featurisations
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470])
table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082])
table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393])
print(table)
SVM with less dimensions with meanResponseCoding worked well
because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel.
Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model.
Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.3 Analysing of Variation Feature
</p>
</div>
</div>
</div>
df.head()
Q1. what is the Variation Feature Type?
</div>
</div>
</div>
It is Categorical
</div>
</div>
</div>
Q2.How many categories are present?
</div>
</div>
</div>
len(df.Variation.value_counts())
There are 2996 categories
</div>
</div>
</div>
Q3.What is the distribution of categories?
</div>
</div>
</div>
unique_variations=df.Variation.value_counts()
sum0f_unique=sum(unique_variations)
histoGram_variation=unique_variations.values / sum0f_unique
plt.plot(histoGram_variation ,label = "Histogram of Variation")
plt.xlabel(" index of Variation")
plt.ylabel(" count")
plt.legend()
plt.grid()
plt.show()
- by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0.
cumsum_histogram=np.cumsum(histoGram_variation)
plt.plot(cumsum_histogram,label='Cumulative distribution of variations')
plt.grid()
plt.legend()
plt.show()
Q4.How to Featurise this variation Feature?
</div>
</div>
</div>
There are few ways:-
1.OneHotEncoding
2.FeatureHasher
3.MeanResponseCoding
</div>
</div>
</div>
- Lets Start with OneHotEncoding of VariationFeature
ds_oneHotencoding=df
ds_meanResponse_coding=df
ds_featureHasher=df
ds_oneHotencoding.columns
ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text'])
ds_oneHotencoding.head()
label_encoder=LabelEncoder()
ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"])
ds_oneHotencoding
featurenames=label_encoder.classes_
ds_oneHotencoding
onehotencoder_Variation=OneHotEncoder()
encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray()
encoded_array
ds_dash=pd.DataFrame(encoded_array , columns=featurenames)
ds_dash.head()
OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1)
OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues'])
OneHotEncoded_ds.head()
OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv")
OneHotEncoded_ds
- Lets create feature hasher for variation features
ds_featureHasher.head()
ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"])
ds_featureHasher.head()
hasher= FeatureHasher(n_features=9,input_type='string')
array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray()
ds_dash_hashed=pd.DataFrame(array_hashed)
hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1)
hashedEncodedFeatureof_variation.head()
hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv")
- Lets go for mean responsecoding of variation feature
df_mean_response_coding.head()
objects_ThisFeature=df_mean_response_coding.Variation.value_counts()
objects_ThisFeature
my_dictionary_varFeature={ }
for feature_name , feature_total_count in objects_ThisFeature.items():
vector_array_features=[ ]
for index in range(1,10):
count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)])
vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90)))
my_dictionary_varFeature[feature_name] = vector_array_features
my_dictionary_varFeature
length_df=len(df_mean_response_coding)
array_of_vectorss=[ ]
for i in range(0,length_df):
if df_mean_response_coding.iloc[i]['Variation'] in my_dictionary_varFeature.keys():
featurename=df_mean_response_coding.iloc[i]['Variation']
array_of_vectorss.append( my_dictionary_varFeature[featurename] )
else:
array_of_vectorss.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
print(array_of_vectorss)
meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss)
meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1)
meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv")
As we have all the vector forms of the featurisations lets proceed with further analysis
Q5.How good is this Variation feature in predicting y_i?
Lets Build simple models to check our selves
- As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library.
1.meanResponse coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
SVM Model
meanResponseCoding_variationFeature.columns
x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values
y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values
x_true_meanresponse
def svm_model_tuning(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
svm_model_tuning(x_true_meanresponse , y_true_meanResponse)
From above graph i select my best C as 1
Testing of the model
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
svm_model_testing(x_true_meanresponse,y_true_meanResponse)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
2.Hashed coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0')
hashedEncodedFeatureof_variation.head()
x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values
y_hashed_true=hashedEncodedFeatureof_variation['Class'].values
We will reuse the same svm functions we defiend earilier
svm_model_tuning(x_hashed_true,y_hashed_true)
from the above graph i can choose my C as 0.0001
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test ))
svm_model_testing(x_hashed_true,y_hashed_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
3.OneHotencoding of variation feature
As this has high dimention lets use logistic regression
</div>
</div>
</div>
oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0')
oneHotEncodedfeaturesof_Variation.head()
oneHotEncodedfeaturesof_Variation.columns
x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values
y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values
LogisticModel
def logistic_tune(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
logistic_tune(x_onehot_true,y_onehot_true)
From the above graph i can choos aplha as 0.01
Testing model
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test ))
logistic_test(x_onehot_true,y_onehot_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets compare all of them
- Lets compare all observations on Variation Feature
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599])
table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479])
table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380])
print(table)
- From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well.
Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.4 Analysing of Text Feature
</p>
</div>
</div>
</div>
Q1.What kind of feature is Text
</div>
</div>
</div>
It is sentance corpus consisting of many sentances.
Q2.how can we featurise it
There are many ways we will work with these two
1.BOW
2.TFIDF
</div>
</div>
</div>
1.Featurising with bow
df.head()
bow_df=df
bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])
array_input=bow_df.Cleaned_text.values
bow_ds=df
vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)
tf=vectoriser.fit(array_input)
vocab_countVectoriser=(tf.vocabulary_)
vocab_countVectoriser.items()
featurenamesforbow=vectoriser.get_feature_names()
featurenamesforbow
array_transformed=vectoriser.transform(array_input)
array_transformedone=array_transformed.toarray()
dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )
dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)
dataframofBowForText.head()
dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")
2.TFIDF featurisation of text data
</div>
</div>
</div>
df.head()
tfidf_df=df
tfidf_ds=df
tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])
tfidf_df
array_input_tfidf=tfidf_df['Cleaned_text'].values
vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)
vectorizer.fit(array_input_tfidf)
len(vectorizer.vocabulary_)
featurenames_tfidf=vectorizer.get_feature_names()
tfidf_vocab=vectorizer.vocabulary_
transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)
transformed_tfidf_csr=transformed_tfidf_csr.toarray()
arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)
tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)
tfidf_textfeature.to_csv("tfidf_textfeatures.csv")
tfidf_textfeature
Q3. Is this Bow,tfidf for text feature Useful in our classification?
</div>
</div>
</div>
Lets find out by tsne and one simple model.
- TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values
y_true_std=dataframofBowForText.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)
intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Tsne for TFIDF data
x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values
y_tfidf_true=tfidf_textfeature.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Lets do analisys based on models for the two featurisations and compare the results
Lets go with simple model like logistic regression for high dimension data
- 1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
oneHot_Logsitic( data,y_true_std)
this one was with standardised data
oneHot_Logsitic( x_std,y_true_std)
this one is without standardised data
from the graphs i can take my alpha as 0.001
Testing on test datasets
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))
logistic_test(x_std,y_true_std)
Above is result on normal data
logistic_test(data,y_true_std)
Above is Result on standardised data
- we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective
Lets look at other featurisations as well.
- 2.TFIDF feauture for text
Using Logisticmodels we bult for previous cases
Tuning
oneHot_Logsitic(x_tfidf_true,y_tfidf_true)
this one was without standardised data
oneHot_Logsitic(data,y_tfidf_true)
this one was with standardised data
From the graphs i can select my alpha as 0.001
Lets Test the model
without standardised data
logistic_test(x_tfidf_true,y_tfidf_true)
with standardised data
logistic_test(data,y_tfidf_true)
Observations of Text featureised models with Standardised data only
</p>
</div>
</div>
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div>
</div>
</div>
Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div>
</div>
</div>
4.0 Modelling
</p>
</div>
</div>
</div>
Confusion Matrix,Precision,Recall
</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
3.1.2 Analysing Gene Feature
</p>
</div>
</div>
</div>
Q1. what type of feature is gene ?
</div>
</div>
</div>
- It is a Categorical Feature
Q2.How many Categories are there?
</div>
</div>
</div>
df.groupby("Gene")["ID"].count()
- we can see that there are 263 unique Gene categories are present
Q3. Distribution of Gene?
</div>
</div>
</div>
unique_genes = df['Gene'].value_counts()
# the top 10 genes that occured most
print(unique_genes.head(10))
sums=sum(unique_genes.values)
result=unique_genes.values/sums
plt.plot(result,label="Histrogram of Genes")
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.grid()
plt.show()
- There are some genes which occur very less , some genes which occur more
plt.plot(np.cumsum(result),label='Cumulative distribution of Genes')
plt.grid()
plt.legend()
plt.show()
- 80 Percent of the genes have index in range 50-75
Q4 How to featurize this Feature? Gene
</div>
</div>
</div>
1.One-Hot Encoding
2.Mean-ResponseCoding
3.FeatureHashing
</div>
</div>
</div>
- Lets Create data frames to work with this
df_one_hot_encoding=df
df_mean_response_coding=df
df_featureHashing=df
- OneHotEncoding
df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text'])
df_one_hot_encoding.head()
labelencode=LabelEncoder()
df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene'])
df_one_hot_encoding
list_feature_labels=list(labelencode.classes_)
df_one_hot_encoding.head()
onehotencoder=OneHotEncoder()
array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray()
data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels)
data_gen.head()
onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 )
onehotencoded_features_gene.Class.value_counts()
finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables'])
finalOneHotEncodedFeaturesOfGene.Class.value_counts()
- As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene).
finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv")
- Feature Hashing for GeneFeature
We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes.
df_featureHashing.head()
df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text'])
df_featureHashing.head()
hasher=FeatureHasher(n_features=9,input_type='string')
hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray()
dataframe_hashed_features=pd.DataFrame(hased_features)
dataframe_hashed_features.head()
finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1)
finalFeatureHashedFeaturesOfGene.Class.value_counts()
finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv")
- Now Lets go for meanResponseCoding
df_mean_response_coding.head()
df_mean_response_coding.Gene.value_counts()
df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text'])
df_mean_response_coding.Gene.value_counts()
df.head()
len(df)
dummy=df_mean_response_coding[0:3321]
objects=dummy.Gene.value_counts()
def myfunction_one(data):
dummy1=data
my_dicti={}
for i,denominator in objects.items():
vector=[]
for k in range (1,10):
count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)])
vector.append( (count + (1*10)) / (denominator + (1*90)) )
my_dicti[i] = vector
return my_dicti
my_diciti=myfunction_one(dummy)
def vectoresCreation(data):
dummy1=data
eachrow_vector_data=[ ]
for index,row in dummy1.iterrows():
if row["Gene"] in dict(dummy1.Gene.value_counts()).keys():
eachrow_vector_data.append(my_diciti[row["Gene"]])
else :
eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
return(eachrow_vector_data)
eachRow_Vector=vectoresCreation(dummy)
df_meanresponse_vectors=pd.DataFrame(eachRow_Vector)
df_meanresponse_vectors.shape
df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1)
df_meanresponse_vectors.Class.value_counts()
df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv")
Q5.How good is this gene feature in predicting y_i?
We can do this in many ways one is by simple plotting and other by simple models.
- Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well.
finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0')
finalFeatureHashedFeaturesOfGene.head()
finalFeatureHashedFeaturesOfGene.shape
finalFeatureHashedFeaturesOfGene.Class.value_counts()
y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values
X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]]
model to test
def SVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM(X_Hashed,y_true_Hashed)
by seeing the above plot we take alpha as 0.001
def SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test))
SVMModel(X_Hashed,y_true_Hashed)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
lets go to meanresponse coding features and apply SVM
finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0')
finalMeanResponseVectorsOfGene.columns
x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']]
y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values
def SVM_meanResponseCoding(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse)
We ca see that i can take c = 1 from above graph Lets go for testing on test data
def SVM_meanResponseCoding_test(var1,var2):
"""
This function is use to build n test SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 =calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well.
comming to Onehotencodded Features we can use logistic regression due to high dimentions
finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0")
finalOneHotFeatures.columns
x_oneHot=finalOneHotFeatures.iloc[:,0:264 ]
y_true_oneHot=finalOneHotFeatures.Class.values
Model
def LogisticReg_tune(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = []
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ]
for i in c:
clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
LogisticReg_tune(x_oneHot,y_true_oneHot)
- by seeing the above graph i can take log loss as 0.1 .
def LogisticReg_test(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = [ ]
logLoss_test=[ ]
clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 = calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test))
LogisticReg_test(x_oneHot,y_true_oneHot)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well.
- Observations on Gene Featurisations
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470])
table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082])
table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393])
print(table)
SVM with less dimensions with meanResponseCoding worked well
because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel.
Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model.
Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.3 Analysing of Variation Feature
</p>
</div>
</div>
</div>
df.head()
Q1. what is the Variation Feature Type?
</div>
</div>
</div>
It is Categorical
</div>
</div>
</div>
Q2.How many categories are present?
</div>
</div>
</div>
len(df.Variation.value_counts())
There are 2996 categories
</div>
</div>
</div>
Q3.What is the distribution of categories?
</div>
</div>
</div>
unique_variations=df.Variation.value_counts()
sum0f_unique=sum(unique_variations)
histoGram_variation=unique_variations.values / sum0f_unique
plt.plot(histoGram_variation ,label = "Histogram of Variation")
plt.xlabel(" index of Variation")
plt.ylabel(" count")
plt.legend()
plt.grid()
plt.show()
- by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0.
cumsum_histogram=np.cumsum(histoGram_variation)
plt.plot(cumsum_histogram,label='Cumulative distribution of variations')
plt.grid()
plt.legend()
plt.show()
Q4.How to Featurise this variation Feature?
</div>
</div>
</div>
There are few ways:-
1.OneHotEncoding
2.FeatureHasher
3.MeanResponseCoding
</div>
</div>
</div>
- Lets Start with OneHotEncoding of VariationFeature
ds_oneHotencoding=df
ds_meanResponse_coding=df
ds_featureHasher=df
ds_oneHotencoding.columns
ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text'])
ds_oneHotencoding.head()
label_encoder=LabelEncoder()
ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"])
ds_oneHotencoding
featurenames=label_encoder.classes_
ds_oneHotencoding
onehotencoder_Variation=OneHotEncoder()
encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray()
encoded_array
ds_dash=pd.DataFrame(encoded_array , columns=featurenames)
ds_dash.head()
OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1)
OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues'])
OneHotEncoded_ds.head()
OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv")
OneHotEncoded_ds
- Lets create feature hasher for variation features
ds_featureHasher.head()
ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"])
ds_featureHasher.head()
hasher= FeatureHasher(n_features=9,input_type='string')
array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray()
ds_dash_hashed=pd.DataFrame(array_hashed)
hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1)
hashedEncodedFeatureof_variation.head()
hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv")
- Lets go for mean responsecoding of variation feature
df_mean_response_coding.head()
objects_ThisFeature=df_mean_response_coding.Variation.value_counts()
objects_ThisFeature
my_dictionary_varFeature={ }
for feature_name , feature_total_count in objects_ThisFeature.items():
vector_array_features=[ ]
for index in range(1,10):
count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)])
vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90)))
my_dictionary_varFeature[feature_name] = vector_array_features
my_dictionary_varFeature
length_df=len(df_mean_response_coding)
array_of_vectorss=[ ]
for i in range(0,length_df):
if df_mean_response_coding.iloc[i]['Variation'] in my_dictionary_varFeature.keys():
featurename=df_mean_response_coding.iloc[i]['Variation']
array_of_vectorss.append( my_dictionary_varFeature[featurename] )
else:
array_of_vectorss.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
print(array_of_vectorss)
meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss)
meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1)
meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv")
As we have all the vector forms of the featurisations lets proceed with further analysis
Q5.How good is this Variation feature in predicting y_i?
Lets Build simple models to check our selves
- As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library.
1.meanResponse coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
SVM Model
meanResponseCoding_variationFeature.columns
x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values
y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values
x_true_meanresponse
def svm_model_tuning(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
svm_model_tuning(x_true_meanresponse , y_true_meanResponse)
From above graph i select my best C as 1
Testing of the model
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
svm_model_testing(x_true_meanresponse,y_true_meanResponse)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
2.Hashed coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0')
hashedEncodedFeatureof_variation.head()
x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values
y_hashed_true=hashedEncodedFeatureof_variation['Class'].values
We will reuse the same svm functions we defiend earilier
svm_model_tuning(x_hashed_true,y_hashed_true)
from the above graph i can choose my C as 0.0001
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test ))
svm_model_testing(x_hashed_true,y_hashed_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
3.OneHotencoding of variation feature
As this has high dimention lets use logistic regression
</div>
</div>
</div>
oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0')
oneHotEncodedfeaturesof_Variation.head()
oneHotEncodedfeaturesof_Variation.columns
x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values
y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values
LogisticModel
def logistic_tune(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
logistic_tune(x_onehot_true,y_onehot_true)
From the above graph i can choos aplha as 0.01
Testing model
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test ))
logistic_test(x_onehot_true,y_onehot_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets compare all of them
- Lets compare all observations on Variation Feature
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599])
table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479])
table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380])
print(table)
- From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well.
Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.4 Analysing of Text Feature
</p>
</div>
</div>
</div>
Q1.What kind of feature is Text
</div>
</div>
</div>
It is sentance corpus consisting of many sentances.
Q2.how can we featurise it
There are many ways we will work with these two
1.BOW
2.TFIDF
</div>
</div>
</div>
1.Featurising with bow
df.head()
bow_df=df
bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])
array_input=bow_df.Cleaned_text.values
bow_ds=df
vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)
tf=vectoriser.fit(array_input)
vocab_countVectoriser=(tf.vocabulary_)
vocab_countVectoriser.items()
featurenamesforbow=vectoriser.get_feature_names()
featurenamesforbow
array_transformed=vectoriser.transform(array_input)
array_transformedone=array_transformed.toarray()
dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )
dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)
dataframofBowForText.head()
dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")
2.TFIDF featurisation of text data
</div>
</div>
</div>
df.head()
tfidf_df=df
tfidf_ds=df
tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])
tfidf_df
array_input_tfidf=tfidf_df['Cleaned_text'].values
vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)
vectorizer.fit(array_input_tfidf)
len(vectorizer.vocabulary_)
featurenames_tfidf=vectorizer.get_feature_names()
tfidf_vocab=vectorizer.vocabulary_
transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)
transformed_tfidf_csr=transformed_tfidf_csr.toarray()
arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)
tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)
tfidf_textfeature.to_csv("tfidf_textfeatures.csv")
tfidf_textfeature
Q3. Is this Bow,tfidf for text feature Useful in our classification?
</div>
</div>
</div>
Lets find out by tsne and one simple model.
- TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values
y_true_std=dataframofBowForText.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)
intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Tsne for TFIDF data
x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values
y_tfidf_true=tfidf_textfeature.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Lets do analisys based on models for the two featurisations and compare the results
Lets go with simple model like logistic regression for high dimension data
- 1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
oneHot_Logsitic( data,y_true_std)
this one was with standardised data
oneHot_Logsitic( x_std,y_true_std)
this one is without standardised data
from the graphs i can take my alpha as 0.001
Testing on test datasets
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))
logistic_test(x_std,y_true_std)
Above is result on normal data
logistic_test(data,y_true_std)
Above is Result on standardised data
- we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective
Lets look at other featurisations as well.
- 2.TFIDF feauture for text
Using Logisticmodels we bult for previous cases
Tuning
oneHot_Logsitic(x_tfidf_true,y_tfidf_true)
this one was without standardised data
oneHot_Logsitic(data,y_tfidf_true)
this one was with standardised data
From the graphs i can select my alpha as 0.001
Lets Test the model
without standardised data
logistic_test(x_tfidf_true,y_tfidf_true)
with standardised data
logistic_test(data,y_tfidf_true)
Observations of Text featureised models with Standardised data only
</p>
</div>
</div>
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div>
</div>
</div>
Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div>
</div>
</div>
4.0 Modelling
</p>
</div>
</div>
</div>
Confusion Matrix,Precision,Recall
</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
Q1. what type of feature is gene ?
</div> </div> </div>- It is a Categorical Feature
Q2.How many Categories are there?
</div> </div> </div>df.groupby("Gene")["ID"].count()
- we can see that there are 263 unique Gene categories are present
Q3. Distribution of Gene?
</div> </div> </div>unique_genes = df['Gene'].value_counts()
# the top 10 genes that occured most
print(unique_genes.head(10))
sums=sum(unique_genes.values)
result=unique_genes.values/sums
plt.plot(result,label="Histrogram of Genes")
plt.xlabel('Index of a Gene')
plt.ylabel('Number of Occurances')
plt.legend()
plt.grid()
plt.show()
- There are some genes which occur very less , some genes which occur more
plt.plot(np.cumsum(result),label='Cumulative distribution of Genes')
plt.grid()
plt.legend()
plt.show()
- 80 Percent of the genes have index in range 50-75
Q4 How to featurize this Feature? Gene
</div> </div> </div>1.One-Hot Encoding
2.Mean-ResponseCoding
3.FeatureHashing
</div> </div> </div>- Lets Create data frames to work with this
df_one_hot_encoding=df
df_mean_response_coding=df
df_featureHashing=df
- OneHotEncoding
df_one_hot_encoding=df_one_hot_encoding.drop(columns=['ID', 'Variation','Class','Cleaned_text'])
df_one_hot_encoding.head()
labelencode=LabelEncoder()
df_one_hot_encoding['Generated_lables' ]=labelencode.fit_transform(df_one_hot_encoding['Gene'])
df_one_hot_encoding
list_feature_labels=list(labelencode.classes_)
df_one_hot_encoding.head()
onehotencoder=OneHotEncoder()
array_generated_onehotfeatures=onehotencoder.fit_transform(df_one_hot_encoding[['Generated_lables']]).toarray()
data_gen= pd.DataFrame(array_generated_onehotfeatures,columns=list_feature_labels)
data_gen.head()
onehotencoded_features_gene=pd.concat([df_one_hot_encoding,data_gen , df['Class']] , axis=1 )
onehotencoded_features_gene.Class.value_counts()
finalOneHotEncodedFeaturesOfGene=onehotencoded_features_gene.drop(columns=['Gene','Generated_lables'])
finalOneHotEncodedFeaturesOfGene.Class.value_counts()
- As we can see now we have one hot encodeed features of our gene features as dataframe(finalOneHotEncodedFeaturesOfGene).
finalOneHotEncodedFeaturesOfGene.to_csv("finalOneHotEncodedFeaturesOfGene.csv")
- Feature Hashing for GeneFeature
We had 263 categories of genes so we can reduce them to half or less our wish better we reduce them to size of 9 As we have 9 classes.
df_featureHashing.head()
df_featureHashing=df_featureHashing.drop(columns=['ID','Variation','Class','Cleaned_text'])
df_featureHashing.head()
hasher=FeatureHasher(n_features=9,input_type='string')
hased_features=hasher.fit_transform(df_featureHashing['Gene']).toarray()
dataframe_hashed_features=pd.DataFrame(hased_features)
dataframe_hashed_features.head()
finalFeatureHashedFeaturesOfGene=pd.concat([dataframe_hashed_features,df['Class']],axis=1)
finalFeatureHashedFeaturesOfGene.Class.value_counts()
finalFeatureHashedFeaturesOfGene.to_csv("finalFeatureHashedFeaturesOfGene.csv")
- Now Lets go for meanResponseCoding
df_mean_response_coding.head()
df_mean_response_coding.Gene.value_counts()
df_mean_response_coding=df_mean_response_coding.drop(columns=['Variation','Cleaned_text'])
df_mean_response_coding.Gene.value_counts()
df.head()
len(df)
dummy=df_mean_response_coding[0:3321]
objects=dummy.Gene.value_counts()
def myfunction_one(data):
dummy1=data
my_dicti={}
for i,denominator in objects.items():
vector=[]
for k in range (1,10):
count=len(dummy1.loc[(dummy1['Gene']==i) & (dummy1['Class']==k)])
vector.append( (count + (1*10)) / (denominator + (1*90)) )
my_dicti[i] = vector
return my_dicti
my_diciti=myfunction_one(dummy)
def vectoresCreation(data):
dummy1=data
eachrow_vector_data=[ ]
for index,row in dummy1.iterrows():
if row["Gene"] in dict(dummy1.Gene.value_counts()).keys():
eachrow_vector_data.append(my_diciti[row["Gene"]])
else :
eachrow_vector_data.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
return(eachrow_vector_data)
eachRow_Vector=vectoresCreation(dummy)
df_meanresponse_vectors=pd.DataFrame(eachRow_Vector)
df_meanresponse_vectors.shape
df_meanresponse_vectors=pd.concat([df_meanresponse_vectors , df["Class"]] ,axis=1)
df_meanresponse_vectors.Class.value_counts()
df_meanresponse_vectors.to_csv("FinalMeanResponseVectorsOfGene.csv")
Q5.How good is this gene feature in predicting y_i?
We can do this in many ways one is by simple plotting and other by simple models.
- Lets perform simple model on our three vectors of features we have lets statrt with hashed features as the features are small we can take knn,svm,dt ,, which works really well when we have less number of features and try svms as well.
finalFeatureHashedFeaturesOfGene=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
finalFeatureHashedFeaturesOfGene=finalFeatureHashedFeaturesOfGene.drop(columns='Unnamed: 0')
finalFeatureHashedFeaturesOfGene.head()
finalFeatureHashedFeaturesOfGene.shape
finalFeatureHashedFeaturesOfGene.Class.value_counts()
y_true_Hashed=finalFeatureHashedFeaturesOfGene['Class'].values
X_Hashed=finalFeatureHashedFeaturesOfGene[["0","1","2","3","4","5","6","7","8"]]
model to test
def SVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM(X_Hashed,y_true_Hashed)
by seeing the above plot we take alpha as 0.001
def SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=0.001,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.001 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.001,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.001,logLoss_test))
SVMModel(X_Hashed,y_true_Hashed)
- we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well
lets go to meanresponse coding features and apply SVM
finalMeanResponseVectorsOfGene=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
finalMeanResponseVectorsOfGene=finalMeanResponseVectorsOfGene.drop(columns='Unnamed: 0')
finalMeanResponseVectorsOfGene.columns
x_meanResponse=finalMeanResponseVectorsOfGene[['0','1','2','3','4','5','6','7','8']]
y_true_meanResponse=finalMeanResponseVectorsOfGene.Class.values
def SVM_meanResponseCoding(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf=SVC(C=i,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
SVM_meanResponseCoding(x_meanResponse,y_true_meanResponse)
We ca see that i can take c = 1 from above graph Lets go for testing on test data
def SVM_meanResponseCoding_test(var1,var2):
"""
This function is use to build n test SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf=SVC(C=1,kernel='rbf',class_weight='balanced',probability=True)
#clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 =calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
SVM_meanResponseCoding_test(x_meanResponse,y_true_meanResponse)
- we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well.
comming to Onehotencodded Features we can use logistic regression due to high dimentions
finalOneHotFeatures=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
finalOneHotFeatures=finalOneHotFeatures.drop(columns="Unnamed: 0")
finalOneHotFeatures.columns
x_oneHot=finalOneHotFeatures.iloc[:,0:264 ]
y_true_oneHot=finalOneHotFeatures.Class.values
Model
def LogisticReg_tune(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = []
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 ]
for i in c:
clf = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
LogisticReg_tune(x_oneHot,y_true_oneHot)
- by seeing the above graph i can take log loss as 0.1 .
def LogisticReg_test(var1,var2):
"""
This Function is used to tune the logistic regression
"""
X=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_train = []
logLoss_cv = [ ]
logLoss_test=[ ]
clf = LogisticRegression(penalty='l2',C= 0.1,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
clf.fit(x_train , y_train)
calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
predict_y3 = calibrated.predict_proba(x_test)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
logLoss_test.append(log_loss(y_test,predict_y3, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 0.1 ,logLoss_train))
print("The Logg loss for cv data with best aplha {} is {}".format( 0.1,logLoss_cv ))
print("The Logg loss for test data with best aplha {} is {}".format( 0.1,logLoss_test))
LogisticReg_test(x_oneHot,y_true_oneHot)
- we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well.
- Observations on Gene Featurisations
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Gene","FeatureHaser","SVM-kernel-RBF",0.001,1.7364,1.7737,1.7470])
table.add_row(["Gene","MeanResponseCoding","SVM-kernel-RBF",1,1.1284,1.1436,1.1082])
table.add_row(["Gene","OneHotEncoding","LogisticRegression",0.1,1.2638,1.3524,1.3393])
print(table)
SVM with less dimensions with meanResponseCoding worked well because it uses kernal rbf, it would have worked also with hashed features as welldue to rbf kernel.
Answer to Question 5 would be how gene is useful in our objective, from results (logloss) we obtained i can say that it is useful feature in our classification model.
Q6. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.3 Analysing of Variation Feature
</p>
</div>
</div>
</div>
df.head()
Q1. what is the Variation Feature Type?
</div>
</div>
</div>
It is Categorical
</div>
</div>
</div>
Q2.How many categories are present?
</div>
</div>
</div>
len(df.Variation.value_counts())
There are 2996 categories
</div>
</div>
</div>
Q3.What is the distribution of categories?
</div>
</div>
</div>
unique_variations=df.Variation.value_counts()
sum0f_unique=sum(unique_variations)
histoGram_variation=unique_variations.values / sum0f_unique
plt.plot(histoGram_variation ,label = "Histogram of Variation")
plt.xlabel(" index of Variation")
plt.ylabel(" count")
plt.legend()
plt.grid()
plt.show()
- by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0.
cumsum_histogram=np.cumsum(histoGram_variation)
plt.plot(cumsum_histogram,label='Cumulative distribution of variations')
plt.grid()
plt.legend()
plt.show()
Q4.How to Featurise this variation Feature?
</div>
</div>
</div>
There are few ways:-
1.OneHotEncoding
2.FeatureHasher
3.MeanResponseCoding
</div>
</div>
</div>
- Lets Start with OneHotEncoding of VariationFeature
ds_oneHotencoding=df
ds_meanResponse_coding=df
ds_featureHasher=df
ds_oneHotencoding.columns
ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text'])
ds_oneHotencoding.head()
label_encoder=LabelEncoder()
ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"])
ds_oneHotencoding
featurenames=label_encoder.classes_
ds_oneHotencoding
onehotencoder_Variation=OneHotEncoder()
encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray()
encoded_array
ds_dash=pd.DataFrame(encoded_array , columns=featurenames)
ds_dash.head()
OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1)
OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues'])
OneHotEncoded_ds.head()
OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv")
OneHotEncoded_ds
- Lets create feature hasher for variation features
ds_featureHasher.head()
ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"])
ds_featureHasher.head()
hasher= FeatureHasher(n_features=9,input_type='string')
array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray()
ds_dash_hashed=pd.DataFrame(array_hashed)
hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1)
hashedEncodedFeatureof_variation.head()
hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv")
- Lets go for mean responsecoding of variation feature
df_mean_response_coding.head()
objects_ThisFeature=df_mean_response_coding.Variation.value_counts()
objects_ThisFeature
my_dictionary_varFeature={ }
for feature_name , feature_total_count in objects_ThisFeature.items():
vector_array_features=[ ]
for index in range(1,10):
count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)])
vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90)))
my_dictionary_varFeature[feature_name] = vector_array_features
my_dictionary_varFeature
length_df=len(df_mean_response_coding)
array_of_vectorss=[ ]
for i in range(0,length_df):
if df_mean_response_coding.iloc[i]['Variation'] in my_dictionary_varFeature.keys():
featurename=df_mean_response_coding.iloc[i]['Variation']
array_of_vectorss.append( my_dictionary_varFeature[featurename] )
else:
array_of_vectorss.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
print(array_of_vectorss)
meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss)
meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1)
meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv")
As we have all the vector forms of the featurisations lets proceed with further analysis
Q5.How good is this Variation feature in predicting y_i?
Lets Build simple models to check our selves
- As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library.
1.meanResponse coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
SVM Model
meanResponseCoding_variationFeature.columns
x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values
y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values
x_true_meanresponse
def svm_model_tuning(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
svm_model_tuning(x_true_meanresponse , y_true_meanResponse)
From above graph i select my best C as 1
Testing of the model
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
svm_model_testing(x_true_meanresponse,y_true_meanResponse)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
2.Hashed coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div>
</div>
</div>
hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0')
hashedEncodedFeatureof_variation.head()
x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values
y_hashed_true=hashedEncodedFeatureof_variation['Class'].values
We will reuse the same svm functions we defiend earilier
svm_model_tuning(x_hashed_true,y_hashed_true)
from the above graph i can choose my C as 0.0001
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test ))
svm_model_testing(x_hashed_true,y_hashed_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets look at other featurisations as well
3.OneHotencoding of variation feature
As this has high dimention lets use logistic regression
</div>
</div>
</div>
oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0')
oneHotEncodedfeaturesof_Variation.head()
oneHotEncodedfeaturesof_Variation.columns
x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values
y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values
LogisticModel
def logistic_tune(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
logistic_tune(x_onehot_true,y_onehot_true)
From the above graph i can choos aplha as 0.01
Testing model
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test ))
logistic_test(x_onehot_true,y_onehot_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got.
Lets compare all of them
- Lets compare all observations on Variation Feature
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599])
table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479])
table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380])
print(table)
- From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well.
Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.4 Analysing of Text Feature
</p>
</div>
</div>
</div>
Q1.What kind of feature is Text
</div>
</div>
</div>
It is sentance corpus consisting of many sentances.
Q2.how can we featurise it
There are many ways we will work with these two
1.BOW
2.TFIDF
</div>
</div>
</div>
1.Featurising with bow
df.head()
bow_df=df
bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])
array_input=bow_df.Cleaned_text.values
bow_ds=df
vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)
tf=vectoriser.fit(array_input)
vocab_countVectoriser=(tf.vocabulary_)
vocab_countVectoriser.items()
featurenamesforbow=vectoriser.get_feature_names()
featurenamesforbow
array_transformed=vectoriser.transform(array_input)
array_transformedone=array_transformed.toarray()
dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )
dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)
dataframofBowForText.head()
dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")
2.TFIDF featurisation of text data
</div>
</div>
</div>
df.head()
tfidf_df=df
tfidf_ds=df
tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])
tfidf_df
array_input_tfidf=tfidf_df['Cleaned_text'].values
vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)
vectorizer.fit(array_input_tfidf)
len(vectorizer.vocabulary_)
featurenames_tfidf=vectorizer.get_feature_names()
tfidf_vocab=vectorizer.vocabulary_
transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)
transformed_tfidf_csr=transformed_tfidf_csr.toarray()
arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)
tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)
tfidf_textfeature.to_csv("tfidf_textfeatures.csv")
tfidf_textfeature
Q3. Is this Bow,tfidf for text feature Useful in our classification?
</div>
</div>
</div>
Lets find out by tsne and one simple model.
- TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values
y_true_std=dataframofBowForText.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)
intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Tsne for TFIDF data
x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values
y_tfidf_true=tfidf_textfeature.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Lets do analisys based on models for the two featurisations and compare the results
Lets go with simple model like logistic regression for high dimension data
- 1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
oneHot_Logsitic( data,y_true_std)
this one was with standardised data
oneHot_Logsitic( x_std,y_true_std)
this one is without standardised data
from the graphs i can take my alpha as 0.001
Testing on test datasets
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))
logistic_test(x_std,y_true_std)
Above is result on normal data
logistic_test(data,y_true_std)
Above is Result on standardised data
- we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective
Lets look at other featurisations as well.
- 2.TFIDF feauture for text
Using Logisticmodels we bult for previous cases
Tuning
oneHot_Logsitic(x_tfidf_true,y_tfidf_true)
this one was without standardised data
oneHot_Logsitic(data,y_tfidf_true)
this one was with standardised data
From the graphs i can select my alpha as 0.001
Lets Test the model
without standardised data
logistic_test(x_tfidf_true,y_tfidf_true)
with standardised data
logistic_test(data,y_tfidf_true)
Observations of Text featureised models with Standardised data only
</p>
</div>
</div>
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div>
</div>
</div>
Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div>
</div>
</div>
4.0 Modelling
</p>
</div>
</div>
</div>
Confusion Matrix,Precision,Recall
</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
df.head()
Q1. what is the Variation Feature Type?
</div> </div> </div>It is Categorical
</div> </div> </div>Q2.How many categories are present?
</div> </div> </div>len(df.Variation.value_counts())
There are 2996 categories
</div> </div> </div>Q3.What is the distribution of categories?
</div> </div> </div>unique_variations=df.Variation.value_counts()
sum0f_unique=sum(unique_variations)
histoGram_variation=unique_variations.values / sum0f_unique
plt.plot(histoGram_variation ,label = "Histogram of Variation")
plt.xlabel(" index of Variation")
plt.ylabel(" count")
plt.legend()
plt.grid()
plt.show()
- by seeing the pdf we can say that the most of the variations which are in range of index(0-100) occur like 25% of the time.Rest are very less occuring their count is so small almost 0.
cumsum_histogram=np.cumsum(histoGram_variation)
plt.plot(cumsum_histogram,label='Cumulative distribution of variations')
plt.grid()
plt.legend()
plt.show()
Q4.How to Featurise this variation Feature?
</div> </div> </div>There are few ways:-
1.OneHotEncoding
2.FeatureHasher
3.MeanResponseCoding
</div> </div> </div>- Lets Start with OneHotEncoding of VariationFeature
ds_oneHotencoding=df
ds_meanResponse_coding=df
ds_featureHasher=df
ds_oneHotencoding.columns
ds_oneHotencoding=ds_oneHotencoding.drop(columns=['ID','Gene','Class','Cleaned_text'])
ds_oneHotencoding.head()
label_encoder=LabelEncoder()
ds_oneHotencoding["Labelencodedvalues"]=label_encoder.fit_transform(df["Variation"])
ds_oneHotencoding
featurenames=label_encoder.classes_
ds_oneHotencoding
onehotencoder_Variation=OneHotEncoder()
encoded_array=onehotencoder_Variation.fit_transform(ds_oneHotencoding[['Labelencodedvalues']]).toarray()
encoded_array
ds_dash=pd.DataFrame(encoded_array , columns=featurenames)
ds_dash.head()
OneHotEncoded_ds=pd.concat([ds_oneHotencoding,ds_dash,df['Class']],axis=1)
OneHotEncoded_ds=OneHotEncoded_ds.drop(columns=['Variation','Labelencodedvalues'])
OneHotEncoded_ds.head()
OneHotEncoded_ds.to_csv("oneHotEncodedfeaturesof_Variation.csv")
OneHotEncoded_ds
- Lets create feature hasher for variation features
ds_featureHasher.head()
ds_featureHasher=ds_featureHasher.drop(columns=['ID',"Gene","Cleaned_text"])
ds_featureHasher.head()
hasher= FeatureHasher(n_features=9,input_type='string')
array_hashed=hasher.fit_transform(ds_featureHasher['Variation']).toarray()
ds_dash_hashed=pd.DataFrame(array_hashed)
hashedEncodedFeatureof_variation=pd.concat([ds_dash_hashed,ds_featureHasher['Class']],axis=1)
hashedEncodedFeatureof_variation.head()
hashedEncodedFeatureof_variation.to_csv("hashedEncodedFeatureof_variation.csv")
- Lets go for mean responsecoding of variation feature
df_mean_response_coding.head()
objects_ThisFeature=df_mean_response_coding.Variation.value_counts()
objects_ThisFeature
my_dictionary_varFeature={ }
for feature_name , feature_total_count in objects_ThisFeature.items():
vector_array_features=[ ]
for index in range(1,10):
count=len(df_mean_response_coding.loc[(df_mean_response_coding['Variation']== feature_name) & (df_mean_response_coding['Class']==index)])
vector_array_features.append(((count) + (1*10))/((feature_total_count) + (1*90)))
my_dictionary_varFeature[feature_name] = vector_array_features
my_dictionary_varFeature
length_df=len(df_mean_response_coding)
array_of_vectorss=[ ]
for i in range(0,length_df):
if df_mean_response_coding.iloc[i]['Variation'] in my_dictionary_varFeature.keys():
featurename=df_mean_response_coding.iloc[i]['Variation']
array_of_vectorss.append( my_dictionary_varFeature[featurename] )
else:
array_of_vectorss.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
print(array_of_vectorss)
meanResponseCoding_variationFeature=pd.DataFrame(array_of_vectorss)
meanResponseCoding_variationFeature= pd.concat([meanResponseCoding_variationFeature , df["Class"]], axis=1)
meanResponseCoding_variationFeature.to_csv("meanResponseCoding_variationFeature.csv")
As we have all the vector forms of the featurisations lets proceed with further analysis
Q5.How good is this Variation feature in predicting y_i?
Lets Build simple models to check our selves
- As we have three Vectorisatins lets create three models for each of them and compare the results with pretty table library.
1.meanResponse coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div> </div> </div>SVM Model
meanResponseCoding_variationFeature.columns
x_true_meanresponse=meanResponseCoding_variationFeature[[0 , 1, 2, 3, 4, 5, 6, 7, 8 ]].values
y_true_meanResponse=meanResponseCoding_variationFeature['Class'].values
x_true_meanresponse
def svm_model_tuning(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model=SVC(C=i,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
svm_model_tuning(x_true_meanresponse , y_true_meanResponse)
From above graph i select my best C as 1
Testing of the model
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=1,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
svm_model_testing(x_true_meanresponse,y_true_meanResponse)
- we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well
2.Hashed coded vectors for variation feature
We will use SVM rbf kernel and see the performance
</div> </div> </div>hashedEncodedFeatureof_variation=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
hashedEncodedFeatureof_variation=hashedEncodedFeatureof_variation.drop( columns = 'Unnamed: 0')
hashedEncodedFeatureof_variation.head()
x_hashed_true=hashedEncodedFeatureof_variation[['0','1','2','3','4','5','6','7','8']].values
y_hashed_true=hashedEncodedFeatureof_variation['Class'].values
We will reuse the same svm functions we defiend earilier
svm_model_tuning(x_hashed_true,y_hashed_true)
from the above graph i can choose my C as 0.0001
def svm_model_testing(var1,var2):
"""
This function is used to create SVM and tune it
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model=SVC(C=0.0001,kernel='rbf',probability=True,class_weight='balanced')
model.fit(x_train,y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain,eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest,eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.0001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.0001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.0001,log_loss_test ))
svm_model_testing(x_hashed_true,y_hashed_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets look at other featurisations as well
3.OneHotencoding of variation feature
As this has high dimention lets use logistic regression
</div> </div> </div>oneHotEncodedfeaturesof_Variation=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
oneHotEncodedfeaturesof_Variation=oneHotEncodedfeaturesof_Variation.drop(columns='Unnamed: 0')
oneHotEncodedfeaturesof_Variation.head()
oneHotEncodedfeaturesof_Variation.columns
x_onehot_true=oneHotEncodedfeaturesof_Variation.iloc[:,0:2996].values
y_onehot_true=oneHotEncodedfeaturesof_Variation.Class.values
LogisticModel
def logistic_tune(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
hyperparameter=[0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in hyperparameter:
model = LogisticRegression(penalty='l2',C= i,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
plt.plot(np.log(hyperparameter) , log_loss_train)
plt.plot(np.log(hyperparameter), log_loss_cv)
plt.scatter(np.log(hyperparameter),log_loss_cv)
plt.title("Loggloss vs hyper paramenter")
plt.xlabel("hyperparameter(C)")
plt.ylabel("Log loss")
plt.grid()
return(plt.show())
logistic_tune(x_onehot_true,y_onehot_true)
From the above graph i can choos aplha as 0.01
Testing model
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.01,max_iter=1000 , class_weight='balanced' ,solver='lbfgs' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.01,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.01,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.01,log_loss_test ))
logistic_test(x_onehot_true,y_onehot_true)
- we can say that model is not overfitting and underfitting by looking at the log loss we got. Lets compare all of them
- Lets compare all observations on Variation Feature
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Variation","FeatureHaser","SVM-kernel-RBF",0.0001,1.7335,1.7534,1.7599])
table.add_row(["Variation","MeanResponseCoding","SVM-kernel-RBF",1,0.1340,0.1479,0.1479])
table.add_row(["Variation","OneHotEncoding","LogisticRegression",0.01,1.4238,1.7444,1.7380])
print(table)
- From the above table we can say that kernel svm perfomed good for mean response features im sure it will have performed well for hashed features as well.
Q6. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?
Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.
3.1.4 Analysing of Text Feature
</p>
</div>
</div>
</div>
Q1.What kind of feature is Text
</div>
</div>
</div>
It is sentance corpus consisting of many sentances.
Q2.how can we featurise it
There are many ways we will work with these two
1.BOW
2.TFIDF
</div>
</div>
</div>
1.Featurising with bow
df.head()
bow_df=df
bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])
array_input=bow_df.Cleaned_text.values
bow_ds=df
vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)
tf=vectoriser.fit(array_input)
vocab_countVectoriser=(tf.vocabulary_)
vocab_countVectoriser.items()
featurenamesforbow=vectoriser.get_feature_names()
featurenamesforbow
array_transformed=vectoriser.transform(array_input)
array_transformedone=array_transformed.toarray()
dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )
dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)
dataframofBowForText.head()
dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")
2.TFIDF featurisation of text data
</div>
</div>
</div>
df.head()
tfidf_df=df
tfidf_ds=df
tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])
tfidf_df
array_input_tfidf=tfidf_df['Cleaned_text'].values
vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)
vectorizer.fit(array_input_tfidf)
len(vectorizer.vocabulary_)
featurenames_tfidf=vectorizer.get_feature_names()
tfidf_vocab=vectorizer.vocabulary_
transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)
transformed_tfidf_csr=transformed_tfidf_csr.toarray()
arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)
tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)
tfidf_textfeature.to_csv("tfidf_textfeatures.csv")
tfidf_textfeature
Q3. Is this Bow,tfidf for text feature Useful in our classification?
</div>
</div>
</div>
Lets find out by tsne and one simple model.
- TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values
y_true_std=dataframofBowForText.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)
intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Tsne for TFIDF data
x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values
y_tfidf_true=tfidf_textfeature.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Lets do analisys based on models for the two featurisations and compare the results
Lets go with simple model like logistic regression for high dimension data
- 1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
oneHot_Logsitic( data,y_true_std)
this one was with standardised data
oneHot_Logsitic( x_std,y_true_std)
this one is without standardised data
from the graphs i can take my alpha as 0.001
Testing on test datasets
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))
logistic_test(x_std,y_true_std)
Above is result on normal data
logistic_test(data,y_true_std)
Above is Result on standardised data
- we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective
Lets look at other featurisations as well.
- 2.TFIDF feauture for text
Using Logisticmodels we bult for previous cases
Tuning
oneHot_Logsitic(x_tfidf_true,y_tfidf_true)
this one was without standardised data
oneHot_Logsitic(data,y_tfidf_true)
this one was with standardised data
From the graphs i can select my alpha as 0.001
Lets Test the model
without standardised data
logistic_test(x_tfidf_true,y_tfidf_true)
with standardised data
logistic_test(data,y_tfidf_true)
Observations of Text featureised models with Standardised data only
</p>
</div>
</div>
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div>
</div>
</div>
Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div>
</div>
</div>
4.0 Modelling
</p>
</div>
</div>
</div>
Confusion Matrix,Precision,Recall
</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
Q1.What kind of feature is Text
</div> </div> </div>It is sentance corpus consisting of many sentances.
Q2.how can we featurise it
There are many ways we will work with these two 1.BOW 2.TFIDF
</div> </div> </div>1.Featurising with bow
df.head()
bow_df=df
bow_df=bow_df.drop(columns=['ID','Gene','Class','Variation'])
array_input=bow_df.Cleaned_text.values
bow_ds=df
vectoriser=CountVectorizer(ngram_range=(1,2),min_df=3,max_features=5000)
tf=vectoriser.fit(array_input)
vocab_countVectoriser=(tf.vocabulary_)
vocab_countVectoriser.items()
featurenamesforbow=vectoriser.get_feature_names()
featurenamesforbow
array_transformed=vectoriser.transform(array_input)
array_transformedone=array_transformed.toarray()
dataframofBowForText=pd.DataFrame(array_transformedone ,columns=featurenamesforbow )
dataframofBowForText=pd.concat([dataframofBowForText , df['Class']] , axis=1)
dataframofBowForText.head()
dataframofBowForText.to_csv("datafraneofBowTextFeature.csv")
2.TFIDF featurisation of text data
</div> </div> </div>df.head()
tfidf_df=df
tfidf_ds=df
tfidf_df=tfidf_df.drop(columns=['ID','Gene','Variation','Class'])
tfidf_df
array_input_tfidf=tfidf_df['Cleaned_text'].values
vectorizer=TfidfVectorizer(ngram_range=(1,2),min_df=3,max_features=1000)
vectorizer.fit(array_input_tfidf)
len(vectorizer.vocabulary_)
featurenames_tfidf=vectorizer.get_feature_names()
tfidf_vocab=vectorizer.vocabulary_
transformed_tfidf_csr=vectorizer.transform(array_input_tfidf)
transformed_tfidf_csr=transformed_tfidf_csr.toarray()
arr=pd.DataFrame(transformed_tfidf_csr ,columns=featurenames_tfidf)
tfidf_textfeature=pd.concat([arr,df["Class"]],axis=1)
tfidf_textfeature.to_csv("tfidf_textfeatures.csv")
tfidf_textfeature
Q3. Is this Bow,tfidf for text feature Useful in our classification?
</div> </div> </div>Lets find out by tsne and one simple model.
- TSNE for BOW TEXT Feature
x_std=dataframofBowForText.iloc[:, 0:5000].values
y_true_std=dataframofBowForText.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_std)
intialisetsne=TSNE(n_components=2,perplexity=30.0,n_iter=500,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df10=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df10,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Tsne for TFIDF data
x_tfidf_true=tfidf_textfeature.iloc[:, 0:5000].values
y_tfidf_true=tfidf_textfeature.Class.values
stdnd=StandardScaler()
data=stdnd.fit_transform(x_tfidf_true)
intialisetsne=TSNE(n_components=2,perplexity=40.0,n_iter=5000,n_jobs=-1,random_state=0)
tsne_data=intialisetsne.fit_transform(data)
concatinated_df12=pd.concat([pd.DataFrame(tsne_data , columns=["Dim1","Dim2"]) , df["Class"]],axis=1)
sns.set_style("whitegrid")
sns.FacetGrid(concatinated_df12,hue='Class',height=5)\
.map(plt.scatter , 'Dim1' , 'Dim2')\
.add_legend()
plt.show()
we cant analyse by seeing this much but i think they may be seperated in high diemension, As of now i can see that class7 and class 4 are grouped near to thier neighbours well
Lets do analisys based on models for the two featurisations and compare the results
Lets go with simple model like logistic regression for high dimension data
- 1.BOW Feature for text
def oneHot_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
#plt.plot((c), train_auc, label='Train AUC')
#plt.plot((c), cv_auc, label='CV AUC')
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV AUC')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
oneHot_Logsitic( data,y_true_std)
this one was with standardised data
oneHot_Logsitic( x_std,y_true_std)
this one is without standardised data
from the graphs i can take my alpha as 0.001
Testing on test datasets
def logistic_test(var1,var2):
"""
This function is used to create model and tune it to find best hyper parameter
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=0.001,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 0.001,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 0.001,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 0.001,log_loss_test ))
logistic_test(x_std,y_true_std)
Above is result on normal data
logistic_test(data,y_true_std)
Above is Result on standardised data
- we can say that model is not overfitting and underfitting by looking at the log loss we got.Yes this type of featurisation is useful in our objective Lets look at other featurisations as well.
- 2.TFIDF feauture for text
Using Logisticmodels we bult for previous cases
Tuning
oneHot_Logsitic(x_tfidf_true,y_tfidf_true)
this one was without standardised data
oneHot_Logsitic(data,y_tfidf_true)
this one was with standardised data
From the graphs i can select my alpha as 0.001
Lets Test the model
without standardised data
logistic_test(x_tfidf_true,y_tfidf_true)
with standardised data
logistic_test(data,y_tfidf_true)
Observations of Text featureised models with Standardised data only
</p>
</div>
</div>
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div>
</div>
</div>
Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div>
</div>
</div>
4.0 Modelling
</p>
</div>
</div>
</div>
Confusion Matrix,Precision,Recall
</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["Text","TFIDF","Logistic",0.001,0.944,1.210,1.153])
table.add_row(["Text","BOW","Logistic",0.001,0.906,1.544,1.214])
print(table)
TFIDF Featurisation is more sensible comparitively than BOW featurisation
Q4.Are these Features stable accross all data sets?
</div> </div> </div>Yes,because there is no much difference in cv n test log loss.
Now we have all the vectors lets go for modelling
</div> </div> </div>
4.0 Modelling
</p>
</div>
</div>
</div>
Confusion Matrix,Precision,Recall
</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
Confusion Matrix,Precision,Recall
</p>
</div>
</div>
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
# representing A in heatmap format
print("-"*20, "Confusion matrix", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
# representing B in heatmap format
print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
plt.figure(figsize=(20,7))
sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
Loading the df and manupulating
#Gene DF
gene_vector_meanResponse=pd.read_csv("/content/FinalMeanResponseVectorsOfGene.csv")
gene_vector_Hashing=pd.read_csv("/content/finalFeatureHashedFeaturesOfGene.csv")
gene_vector_oneHot=pd.read_csv("/content/finalOneHotEncodedFeaturesOfGene.csv")
#Variation DF
variation_df_meanResponse=pd.read_csv("/content/meanResponseCoding_variationFeature.csv")
variation_df_oneHot=pd.read_csv("/content/oneHotEncodedfeaturesof_Variation.csv")
variation_df_featureHashing=pd.read_csv("/content/hashedEncodedFeatureof_variation.csv")
#Text DF
text_df_tfidf=pd.read_csv("/content/tfidf_textfeatures.csv")
text_df_bow=pd.read_csv("/content/datafraneofBowTextFeature.csv")
#originalDF
df=pd.read_csv("/content/OriginalDFWithCleanedTextPresent.csv")
variation_df_featureHashing.columns
gene_vector_meanResponse=gene_vector_meanResponse.drop(columns=['Unnamed: 0','Class'])
gene_vector_oneHot=gene_vector_oneHot.drop(columns=['Unnamed: 0','Class'])
gene_vector_Hashing=gene_vector_Hashing.drop(columns=['Unnamed: 0','Class'])
variation_df_meanResponse=variation_df_meanResponse.drop(columns=['Unnamed: 0','Class'])
variation_df_oneHot=variation_df_oneHot.drop(columns=['Unnamed: 0','Class'])
variation_df_featureHashing=variation_df_featureHashing.drop(columns=['Unnamed: 0','Class'])
text_df_bow=text_df_bow.drop(columns=['Unnamed: 0','Class'])
text_df_tfidf=text_df_tfidf.drop(columns=['Unnamed: 0','Class'])
df=df.drop(columns=['Unnamed: 0'])
gene_vector_meanResponse.columns
variation_df_meanResponse.columns
text_df_tfidf.columns
#1.(gene meanResponse + variation meanResponse + text tfidf(uniPlusbigram with top 1k words)) standardised version
#2.gene OneHot + variation onehot + (text bow(uniPlusBigram(5kwords))) standardised version
#3.(gene meanResponse + variation meanResponse + text bow(uniPlusBigram(5kwords)) standardised version
#4.gene onehot + variation onehot + (tiidf text(uniplusbigramwith1kwords)) standardised
#5.(gene featurehashed + variation featureHashed + text bow(uniPlusBigram(5kwords)) Standardised version
#6.(gene featurehashed + variation featureHashed + text tfidf(uniPlusBigram(1kwords)) Standardised version
#and many more combinations can be done,, we will focus mailny on tfidf version and mean response
##coding and featurehashing for one baseline model and do other models with tfidf meanResponse vectors
### srikanth sir perfomed the onehot encoding n bow(unigrams) in video lectures.
gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf],axis=1)
withoutstd_gene_var_text_meanTdidf=pd.concat([gene_vector_meanResponse,variation_df_meanResponse,text_df_tfidf,df['Class']],axis=1)
gene_var_text_meanTdidf_colums=gene_var_text_meanTdidf.columns
strndzn=StandardScaler()
std_gene_var_text_meanTdidf=strndzn.fit_transform(gene_var_text_meanTdidf)
std_gene_var_text_meanTdidf=pd.concat([pd.DataFrame(std_gene_var_text_meanTdidf,columns=gene_var_text_meanTdidf_colums),df["Class"]],axis=1)
std_gene_var_text_meanTdidf
std_gene_var_text_meanTdidf.to_csv("gvt_mt_final.csv")
gene_vector_oneHot,variation_df_oneHot,text_df_tfidf
clmns=text_df_tfidf.columns
text_stdr=strndzn.fit_transform(text_df_tfidf)
df1=pd.DataFrame(text_stdr,columns=clmns)
std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,df1,df['Class']],axis=1)
without_std_gene_var_text_onehottifidf=pd.concat([gene_vector_oneHot,variation_df_oneHot,text_df_tfidf,df["Class"]],axis=1)
std_gene_var_text_onehottifidf.to_csv('gvt_ot_final.csv')
std_gene_var_text_onehottifidf
gene_vector_Hashing,variation_df_featureHashing,text_df_tfidf
std_gene_var_text_hashingtfidf=pd.concat([gene_vector_Hashing,variation_df_featureHashing,df1,df['Class']],axis=1)
std_gene_var_text_hashingtfidf.to_csv('gvt_ht.csv')
std_gene_var_text_hashingtfidf
Now we have our data sets lets perform modelling.
Datasets info:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams) we will use this hashed feature for upcomming models.but not for naive bayes because it doesnt calculate -ve probabilities.
Lets Create Models
4.1 NaiveBayes Model
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_NB=without_std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_NB=without_std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_NB)
def naiveBayesTunning(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
Alpha = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 , 100 , 1000 , 10000 , 100000]
for i in Alpha:
MNB = MultinomialNB(alpha=i)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(np.log(Alpha), train_logloss, label='Train Logloss')
plt.plot(np.log(Alpha), cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(np.log(Alpha),cv_logloss , label='CV logloss')
plt.xlabel("log(alpha)")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
naiveBayesTunning(varA_NB,varB_NB)
From the above graph i can take my alpha value as 1.
def naiveBayesTesting_(var1,var2):
"""
This function is used to tune naiveBayes Model on data and find right Hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
MNB = MultinomialNB(alpha=1)
MNB.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(MNB, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,train_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,cv_logloss))
print("The hyper parameter and logloss for Train data are :{} and {}".format(1,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
naiveBayesTesting_(varA_NB,varB_NB)
- This is the Base Line Model Results it performed well.
withoutstd_gene_var_text_meanTdidf
varA_NB_=without_std_gene_var_text_onehottifidf.iloc[:,0:1018].values
varB_NB_=without_std_gene_var_text_onehottifidf.Class.values
Reusing the above functions
naiveBayesTunning(varA_NB_ , varB_NB_)
from above graph i can select alpha as 1
naiveBayesTesting_(varA_NB_ , varB_NB_)
4.2 Knn Model
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
dash_dfff=std_gene_var_text_onehottifidf
varA_Knn=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_knn=std_gene_var_text_onehottifidf.Class.values
from collections import Counter
Counter(varB_knn)
Balancing the data set is important in knn as knn doesnt have balancing option in model parameters
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(varA_Knn,varB_knn)
Counter(y_res)
def knnTuning(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
K = [1, 5, 11, 15, 21, 31, 41, 51 , 61 ,81]
for i in K:
neigh = KNeighborsClassifier(n_neighbors=i,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
#y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
#test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
plt.plot(K, train_logloss, label='Train Logloss')
plt.plot(K, cv_logloss ,label='CV logloss')
plt.legend()
plt.scatter(K,cv_logloss , label='CV logloss')
plt.xlabel("K")
plt.ylabel("logloss")
plt.title("ERROR PLOTS")
return(plt.show())
knnTuning(x_res,y_res)
From above graph i can select my k value as 31
def knnTesting(var1, var2):
"""
This Function is used to perform KNNtuning and decide the K hyper parameter
"""
X=var1
Y=var2
#Splitting the data
x_train,x_test,y_train,y_test=train_test_split(X,Y, stratify=Y ,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train, stratify=y_train ,test_size=0.2)
#hyperTuning
train_logloss = []
cv_logloss = []
test_logloss = []
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf.fit(x_train,y_train)
y_train_pred = sig_clf.predict_proba(x_train)
y_cv_pred = sig_clf.predict_proba(x_cv)
y_test_pred = sig_clf.predict_proba(x_test)
train_logloss.append( log_loss(y_train,y_train_pred,eps=1e-15))
cv_logloss.append(log_loss(y_cv,y_cv_pred,eps=1e-15))
test_logloss.append(log_loss(y_test,y_test_pred,eps=1e-15))
print("The hyper parameter and logloss for Train data are :{} and {}".format(15,train_logloss))
print("The hyper parameter and logloss for cv data are :{} and {}".format(15,cv_logloss))
print("The hyper parameter and logloss for Test data are :{} and {}".format(31,test_logloss))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
knnTesting(x_res,y_res)
std_gene_var_text_meanTdidf
x_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
y_mean=std_gene_var_text_meanTdidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_mean,y_mean)
from collections import Counter
Counter(y_res)
Tuning using the previous functions
knnTuning(x_res,y_res)
from the above graph i can take k =21
knnTesting(x_res,y_res)
std_gene_var_text_hashingtfidf
x_hashed=std_gene_var_text_hashingtfidf.iloc[:, 0:1018].values
y_hashed=std_gene_var_text_hashingtfidf.Class.values
from imblearn.combine import SMOTETomek
smk=SMOTETomek(random_state=42)
x_res,y_res=smk.fit_sample(x_hashed,y_hashed)
from collections import Counter
Counter(y_res)
tuning model and testing model with old functions created above
knnTuning(x_res,y_res)
From the graph i can take k as 15
knnTesting(x_res,y_res)
4.3 Logistic Regression
</p>
</div>
</div>
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
</div>
</div>
</div>
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
varA_log=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varB_log=std_gene_var_text_onehottifidf.Class.values
def tune_Logsitic(var1,var2):
"""
This function is use to build n hyperparamater tune logisticmodel
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='log',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tune_Logsitic(varA_log,varB_knn)
From the graph i can take aplha as 1
def test_Logistic(var1,var2):
"""
This function is used to test model on test data.
"""
x_true=var1
y_true=var2
x_train,x_test,y_train,y_test=train_test_split(x_true,y_true,stratify=y_true,test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train,stratify=y_train,test_size=0.2)
print("The shape of the train n test vector as follows:")
print(x_train.shape,y_train.shape)
print(x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
log_loss_train=[]
log_loss_cv=[]
log_loss_test=[]
model = LogisticRegression(penalty='l2',C=1,max_iter=1000 , class_weight='balanced' ,solver='newton-cg' )
model.fit(x_train , y_train)
clf=CalibratedClassifierCV(model,method='sigmoid',cv=5)
clf.fit(x_train,y_train)
pred_ytrain=clf.predict_proba(x_train)
log_loss_train.append(log_loss(y_train,pred_ytrain , eps=1e-15))
pred_ycv=clf.predict_proba(x_cv)
log_loss_cv.append(log_loss(y_cv,pred_ycv,eps=1e-15))
pred_ytest=clf.predict_proba(x_test)
log_loss_test.append(log_loss(y_test,pred_ytest , eps=1e-15))
print("The Logloss for {} the coresponding train loss is {}".format( 1,log_loss_train ))
print("The Logloss for {} the coresponding cv loss is {}".format( 1,log_loss_cv ))
print("The Logloss for {} the coresponding test loss is {}".format( 1,log_loss_test ))
plot_confusion_matrix(y_test, clf.predict(x_test))
test_Logistic(varA_log,varB_log)
std_gene_var_text_meanTdidf
vara_mean=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean=std_gene_var_text_meanTdidf.Class.values
for tunning and testing using above functions
tune_Logsitic(vara_mean,varb_mean)
from the above graph i can conclude c=1
test_Logistic(vara_mean,varb_mean)
std_gene_var_text_hashingtfidf
vara_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed=std_gene_var_text_hashingtfidf.Class.values
tune_Logsitic(vara_hashed,varb_hashed)
from graph i can take C as 1
test_Logistic(vara_hashed,varb_hashed)
4.4 SVM
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
vara_svm=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_svm=std_gene_var_text_onehottifidf.Class.values
def tuneSVM(var1,var2):
"""
This function is use to build n hyperparamater tune SVM
"""
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
#Svm HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
c = [0.000001 , 0.00001 , 0.0001 , 0.001 , 0.01 , 0.1 , 1 , 10 ]
for i in c:
clf_SGD=SGDClassifier(loss='hinge',penalty='l2',alpha=i,class_weight='balanced')
clf_SGD.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y = calibrated.predict_proba(x_train)
predict_y2 = calibrated.predict_proba(x_cv)
logLoss_train.append(log_loss(y_train,predict_y, eps=1e-15))
logLoss_cv.append(log_loss(y_cv,predict_y2, eps=1e-15))
plt.plot(np.log(c), logLoss_train, label='Train logloss')
plt.plot(np.log(c), logLoss_cv, label='CV logloss')
plt.scatter(np.log(c), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C: hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
tuneSVM(vara_svm,varb_svm)
from the graph i can select c = 0.1
def test_SVMModel(var1 , var2):
X = var1
y_true = var2
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
logLoss_test=[ ]
logLoss_cv_new=[ ]
logLoss_train_new=[ ]
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1,class_weight='balanced')
clf_SGD_test.fit(x_train,y_train)
calibrated = CalibratedClassifierCV(clf_SGD_test, method='sigmoid', cv=5)
calibrated.fit(x_train , y_train)
predict_y_test = calibrated.predict_proba(x_test)
logLoss_test.append(log_loss(y_test,predict_y_test, eps=1e-15))
predicted_y_train=calibrated.predict_proba(x_train)
logLoss_train_new.append(log_loss(y_train,predicted_y_train, eps=1e-15))
predicted_y_cv=calibrated.predict_proba(x_cv)
logLoss_cv_new.append(log_loss(y_cv,predicted_y_cv, eps=1e-15))
print("The Logg loss for training data with best aplha {} is {} ".format( 1 ,logLoss_train_new ))
print("The Logg loss for cv data with best aplha {} is {}".format( 1,logLoss_cv_new ))
print("The Logg loss for test data with best aplha {} is {}".format( 1,logLoss_test))
plot_confusion_matrix(y_test, calibrated.predict(x_test))
test_SVMModel(vara_svm,varb_svm)
std_gene_var_text_meanTdidf
vara_mean_svm=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
varb_mean_svm=std_gene_var_text_meanTdidf.Class.values
using previous functions for model
tuneSVM(vara_mean_svm,varb_mean_svm)
from above c =1
test_SVMModel(vara_mean_svm,varb_mean_svm)
std_gene_var_text_hashingtfidf
vara_svm_hashed=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_svm_hashed=std_gene_var_text_hashingtfidf.Class.values
tuneSVM(vara_svm_hashed,varb_svm_hashed)
from above graph i can take c as 1
test_SVMModel(vara_svm_hashed,varb_hashed)
4.5 Random Forest
</p>
DatasetsUsedHere:-
-
gene (onehot encoded) + variation (oneHot encoded) +text(tfidf-Bigrams)
-
gene(meanResponse encoded) + variation (meanResponse encoded) + text(tfidf-Bigrams)
-
gene(hashed) + variation(hashed) + text(tfidf-Bigrams)
std_gene_var_text_onehottifidf
vara_RF_onhot=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
varb_RF_onhot=std_gene_var_text_onehottifidf.Class.values
def tune_randomforest(vara,varb):
"""
This function is used to tune rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
estimators = [100,200,500,1000,2000]
max_depth = [3, 5, 7,10]
for i in estimators:
for j in max_depth:
print("for n_estimators =", i,"and max depth = ", j)
clf = RandomForestClassifier(n_estimators=i, criterion='gini',class_weight='balanced', max_depth=j, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
tune_randomforest(vara_RF_onhot,varb_RF_onhot)
from above data i can select esimators 2000 and depth=5
def testRF(vara,varb):
"""
This function is used to test rf model
"""
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
# HyperTuning
logLoss_train=[ ]
logLoss_cv=[ ]
logLoss_test=[ ]
clf = RandomForestClassifier(n_estimators=1000, criterion='gini',class_weight='balanced', max_depth=5, random_state=42, n_jobs=-1)
clf.fit(x_train, y_train)
sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
sig_clf.fit(x_train, y_train)
sig_clf_probs_ytrain = sig_clf.predict_proba(x_train)
sig_clf_probs_ycv = sig_clf.predict_proba(x_cv)
sig_clf_probs_ytest = sig_clf.predict_proba(x_test)
logLoss_cv.append(log_loss(y_cv, sig_clf_probs_ycv, labels=clf.classes_, eps=1e-15))
logLoss_train.append(log_loss(y_train, sig_clf_probs_ytrain, labels=clf.classes_, eps=1e-15))
logLoss_test.append(log_loss(y_test, sig_clf_probs_ytest, labels=clf.classes_, eps=1e-15))
print("Log Loss cv:",log_loss(y_cv, sig_clf_probs_ycv))
print("Log Loss train:",log_loss(y_train,sig_clf_probs_ytrain ))
print("Log Loss test:",log_loss(y_test,sig_clf_probs_ytest ))
plot_confusion_matrix(y_test, sig_clf.predict(x_test))
testRF(vara_RF_onhot,varb_RF_onhot)
std_gene_var_text_meanTdidf
vara_mean_rf=std_gene_var_text_meanTdidf.iloc[:,0:1018]
varb_mean_rf=std_gene_var_text_meanTdidf.Class.values
tune_randomforest(vara_mean_rf,varb_mean_rf)
from the above i can take estimators as 2000 and depth as 3
testRF(vara_mean_rf,varb_mean_rf)
std_gene_var_text_hashingtfidf
vara_hashed_rf=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
varb_hashed_rf=std_gene_var_text_hashingtfidf.Class.values
tune_randomforest(vara_hashed_rf,varb_hashed_rf)
from looking above i can take estimators as 1000 and depth as 5
testRF(vara_hashed_rf,varb_hashed_rf)
4.6 Lets apply Stacking classifier
</p>
</div>
</div>
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
X=std_gene_var_text_meanTdidf.iloc[:,0:1018].values
Y=std_gene_var_text_meanTdidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(X,Y)
Models
def stackingClassifier(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=0.1,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
log_error = log_loss(y_train, sF.predict_proba(x_train),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
log_error = log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15)
print("Log loss (cv) on the stacking classifier :",log_error)
log_error = log_loss(y_test, sF.predict_proba(x_test),eps=1e-15)
print("Log loss (train) on the stacking classifier :",log_error)
plot_confusion_matrix(y_test , sF.predict(x_test))
stackingClassifier(xres,yres)
def stackingClassifier_tunning(vara,varb):
X = vara
y_true = varb
x_train,x_test,y_train,y_test=train_test_split(X , y_true , stratify=y_true , test_size=0.2)
x_train,x_cv,y_train,y_cv=train_test_split(x_train,y_train , stratify=y_train , test_size=0.2)
print ( x_train.shape,y_train.shape)
print( x_cv.shape,y_cv.shape)
print(x_test.shape,y_test.shape)
hp=[0.0001,0.001,0.01,0.1,1,10,15]
logLoss_train=[]
logLoss_test=[]
logLoss_cv=[]
for i in hp:
neigh = KNeighborsClassifier(n_neighbors=15,n_jobs=-1)
neigh.fit(x_train, y_train)
sig_clf0 = CalibratedClassifierCV(neigh, method="sigmoid")
sig_clf0.fit(x_train,y_train)
model = LogisticRegression(penalty='l2',C=0.1,max_iter=1000 ,solver='newton-cg' )
model.fit(x_train , y_train)
sig_clf1 = CalibratedClassifierCV(model, method="sigmoid")
sig_clf1.fit(x_train,y_train)
clf_SGD_test=SGDClassifier(loss='hinge',penalty='l2',alpha=1)
clf_SGD_test.fit(x_train,y_train)
sig_clf2 = CalibratedClassifierCV(clf_SGD_test, method="sigmoid")
sig_clf2.fit(x_train,y_train)
meta_clf=LogisticRegression(penalty='l2',C=i,solver='newton-cg',n_jobs=-1,max_iter=4000)
sF = StackingClassifier(classifiers=[sig_clf0, sig_clf1, sig_clf2], meta_classifier=meta_clf, use_probas=True)
sF.fit(x_train, y_train)
logLoss_train.append(log_loss(y_train, sF.predict_proba(x_train),eps=1e-15))
logLoss_cv.append(log_loss(y_cv, sF.predict_proba(x_cv),eps=1e-15))
#logLoss_train.append(log_loss(y_test, sF.predict_proba(x_test),eps=1e-15))
plt.plot(np.log(hp), logLoss_train, label='Train logloss')
plt.plot(np.log(hp), logLoss_cv, label='CV logloss')
plt.scatter(np.log(hp), logLoss_cv, label='CV logloss')
plt.legend()
plt.xlabel("C:hyperparameter")
plt.ylabel("LogLoss")
plt.title("ERROR PLOTS")
return(plt.show())
stackingClassifier_tunning(xres,yres)
for meta classifier i can take my c values as 0.1
x=std_gene_var_text_onehottifidf.iloc[:,0:4260].values
y=std_gene_var_text_onehottifidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
reusing above functions
im skipping to apply stacking with one hot features as it is taking more time for tuning stage directly applying test stage with previous models parameters.
stackingClassifier_tunning(xres,yres)
taking c as 1
stackingClassifier(xres,yres)
taking c as 0.1
stackingClassifier(xres,yres)
std_gene_var_text_hashingtfidf
x=std_gene_var_text_hashingtfidf.iloc[:,0:1018].values
y=std_gene_var_text_hashingtfidf.Class.values
smk=SMOTETomek(random_state=42)
xres,yres=smk.fit_sample(x,y)
stackingClassifier_tunning(xres,yres)
from above figure i will select c as 0.1
stackingClassifier(xres,yres)
5.0 Final Observations Preetytableformat
</p>
</div>
</div>
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.
</div>
from prettytable import PrettyTable
table=PrettyTable()
table.field_names=["Feature Name" , "Featurisation Used" ,"Model","HyperParameter", " train Logloss"," Cv Logloss","test Logloss"]
table.add_row(["gene,text,variation","Hashing","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.313,0.526,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.310,0.534,0.526 ])
table.add_row(["gene,text,variation","OneHotEncoding","StackingClassifier","(1 Metaclassifier-LogisticRegression)",0.309,0.531,0.527])
table.add_row(["gene,text,variation","MeanResponseCoding","StackingClassifier","(0.1 Metaclassifier-LogisticRegression)",0.140,0.242,0.259])
table.add_row(["gene,text,variation","Hashing","RandomForestClassifier","(estimatores-1000,depth-5)",0.884,1.125,1.140])
#print("**************************************************************************************************************************")
#bestone
table.add_row(["gene,text,variation","MeanResponseCoding","RandomForestClassifier","(estimatores-2000,depth-3)",0.129,0.161,0.172])
#print("**************************************************************************************************************************")
table.add_row(["gene,text,variation","onehotencoding","RandomForestClassifier","(estimatores-2000,depth-5)",0.900,1.117,1.147])
table.add_row(["gene,text,variation","Hashing","SVM Classifier",1,0.972,1.133,1.203])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",1,0.878,1.104,1.057])
table.add_row(["gene,text,variation","MeanResponseCoding","SVM Classifier",0.1,0.936,1.151,1.168])
table.add_row(["gene,text,variation","Hashed","LogisticRegression",1,0.873,1.192,1.199])
table.add_row(["gene,text,variation","MeanResponseCoding","LogisticRegression",1,0.584,0.857,0.821])
table.add_row(["gene,text,variation","onehotencoding","LogisticRegression",1,0.785,1.196,1.748])
table.add_row(["gene,text,variation","hashed","KNN Clasifier",15,0.647,0.751,0.736])
table.add_row(["gene,text,variation","MeanResponseCoding","KNN Clasifier",31,0.681,0.701,0.777])
table.add_row(["gene,text,variation","onehotencoding","KNN Clasifier",31,0.813,0.878,0.881])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,1.038,1.261,1.232])
table.add_row(["gene,text,variation","MeanResponseCoding","NB Clasifier",1,0.963,1.192,1.198])
print(table)
Out of all models RandomForest perfomed well with meanResponsecoded features, next comes Stacking classifiers and KNN with balancing data set.